{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 前程无忧招聘网站数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目说明\n",
    "##### 在前程无忧招聘网站中搜索数据分析、数据挖掘、算法、人工智能、机器学习、深度学习关键词，获取各个岗位的招聘信息，通过分析，了解公司的招聘信息。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提出问题\n",
    "##### 1.不同岗位数量在全国范围内所占的比例\n",
    "##### 2.每个岗位薪酬和工作经验间的关系\n",
    "##### 3.全国不同城市，针对3种最热门岗位的岗位需求量\n",
    "##### 4.不同规模的公司，对于人工智能岗位的薪酬待遇\n",
    "##### 5.不同规模公司，对人工智能岗位的经验和学历要求\n",
    "##### 6.分析热门城市的招聘岗位特征：公司分布、招聘岗位数量、公司类型、薪资范围"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入常用库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>attribute</th>\n",
       "      <th>company</th>\n",
       "      <th>education</th>\n",
       "      <th>experience</th>\n",
       "      <th>general_situation</th>\n",
       "      <th>hiring_num</th>\n",
       "      <th>industry</th>\n",
       "      <th>members</th>\n",
       "      <th>position</th>\n",
       "      <th>record_date</th>\n",
       "      <th>salary</th>\n",
       "      <th>tag</th>\n",
       "      <th>time</th>\n",
       "      <th>title</th>\n",
       "      <th>welfare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>重庆-江北区</td>\n",
       "      <td>上市公司</td>\n",
       "      <td>平安普惠企业管理有限公司重庆分公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>平安普惠业务集群（以下简称“平安普惠”）是中国平安保险（集团）股份有限公司联营公司旗下成员，...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>金融/投资/证券,银行</td>\n",
       "      <td>1000-5000人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>6-8千/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-28发布</td>\n",
       "      <td>1734LO-数据分析岗 (职位编号：1734LO)</td>\n",
       "      <td>五险一金,年底双薪,定期体检</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海-青浦区</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>上海裕络物流有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>公司介绍,裕络物流于2008年创立上海，主要从事国内第三方物流业务包括行业物流解决方案、多式...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>交通/运输/物流</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>0.8-1.5万/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>运营质量及数据分析经理</td>\n",
       "      <td>五险一金,年终奖金,通讯补贴,餐饮补贴</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州-白云区</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>广州创信网络科技有限公司</td>\n",
       "      <td>大专</td>\n",
       "      <td>1年经验</td>\n",
       "      <td>广州创信网络科技有限公司成立于2018年，集软件研发、AI智能为一体的信息技术解决服务提供商...</td>\n",
       "      <td>招若干人</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>0.7-1万/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>产品销售数据分析助理+双休</td>\n",
       "      <td>五险一金,员工旅游,年终奖金,绩效奖金,定期体检,弹性工作,周末双休</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>扬州-邗江区</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>扬州亿和帽业有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>我们围绕帽子构建起一流的设计、营销能力，并已将产品拓展到手套、围巾等时尚服饰。通过遍...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>互联网/电子商务,服装/纺织/皮革</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>岗位职责：,1、负责定期对公司经营数据进行整理和分析；,2、制作相关报表，并形成日、周、月数...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>3-6千/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>数据分析专员</td>\n",
       "      <td>周休一天半,节日福利,绩效奖金,餐饮补贴,员工旅游,出国机会</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>广州-番禺区</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>比欧(深圳)贸易有限公司广州分公司</td>\n",
       "      <td>大专</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>一、公司简介,比欧(深圳)贸易有限公司成立于2013年，位于广州市番禺区大石礼村南路5号，是...</td>\n",
       "      <td>招2人</td>\n",
       "      <td>服装/纺织/皮革,贸易/进出口</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>1、负责电商平台运营数据的分析工作，针对数据要求提取、汇总数据，提供分析报告及优化建议；,2...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>0.8-1万/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>商品数据分析师</td>\n",
       "      <td>全勤奖,节日福利,下午茶,专业培训,员工旅游</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  address attribute            company education experience  \\\n",
       "0  重庆-江北区      上市公司  平安普惠企业管理有限公司重庆分公司        本科       1年经验   \n",
       "1  上海-青浦区      民营公司         上海裕络物流有限公司        本科     3-4年经验   \n",
       "2  广州-白云区      民营公司       广州创信网络科技有限公司        大专       1年经验   \n",
       "3  扬州-邗江区      民营公司         扬州亿和帽业有限公司        本科       2年经验   \n",
       "4  广州-番禺区      民营公司  比欧(深圳)贸易有限公司广州分公司        大专       2年经验   \n",
       "\n",
       "                                   general_situation hiring_num  \\\n",
       "0  平安普惠业务集群（以下简称“平安普惠”）是中国平安保险（集团）股份有限公司联营公司旗下成员，...        招1人   \n",
       "1  公司介绍,裕络物流于2008年创立上海，主要从事国内第三方物流业务包括行业物流解决方案、多式...        招1人   \n",
       "2  广州创信网络科技有限公司成立于2018年，集软件研发、AI智能为一体的信息技术解决服务提供商...       招若干人   \n",
       "3      我们围绕帽子构建起一流的设计、营销能力，并已将产品拓展到手套、围巾等时尚服饰。通过遍...        招1人   \n",
       "4  一、公司简介,比欧(深圳)贸易有限公司成立于2013年，位于广州市番禺区大石礼村南路5号，是...        招2人   \n",
       "\n",
       "            industry     members  \\\n",
       "0        金融/投资/证券,银行  1000-5000人   \n",
       "1           交通/运输/物流    150-500人   \n",
       "2              计算机软件     50-150人   \n",
       "3  互联网/电子商务,服装/纺织/皮革     50-150人   \n",
       "4    服装/纺织/皮革,贸易/进出口    150-500人   \n",
       "\n",
       "                                            position record_date      salary  \\\n",
       "0                                                NaN  2021-05-29      6-8千/月   \n",
       "1                                                NaN  2021-05-29  0.8-1.5万/月   \n",
       "2                                                NaN  2021-05-29    0.7-1万/月   \n",
       "3  岗位职责：,1、负责定期对公司经营数据进行整理和分析；,2、制作相关报表，并形成日、周、月数...  2021-05-29      3-6千/月   \n",
       "4  1、负责电商平台运营数据的分析工作，针对数据要求提取、汇总数据，提供分析报告及优化建议；,2...  2021-05-29    0.8-1万/月   \n",
       "\n",
       "    tag     time                       title  \\\n",
       "0  数据分析  05-28发布  1734LO-数据分析岗 (职位编号：1734LO)   \n",
       "1  数据分析  05-29发布                 运营质量及数据分析经理   \n",
       "2  数据分析  05-29发布               产品销售数据分析助理+双休   \n",
       "3  数据分析  05-29发布                      数据分析专员   \n",
       "4  数据分析  05-29发布                     商品数据分析师   \n",
       "\n",
       "                              welfare  \n",
       "0                      五险一金,年底双薪,定期体检  \n",
       "1                 五险一金,年终奖金,通讯补贴,餐饮补贴  \n",
       "2  五险一金,员工旅游,年终奖金,绩效奖金,定期体检,弹性工作,周末双休  \n",
       "3      周休一天半,节日福利,绩效奖金,餐饮补贴,员工旅游,出国机会  \n",
       "4              全勤奖,节日福利,下午茶,专业培训,员工旅游  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#导入数据集\n",
    "job=pd.read_csv('job.csv',encoding='utf-8')\n",
    "job.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167670, 16)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据集的总体情况\n",
    "job.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['address', 'attribute', 'company', 'education', 'experience',\n",
       "       'general_situation', 'hiring_num', 'industry', 'members', 'position',\n",
       "       'record_date', 'salary', 'tag', 'time', 'title', 'welfare'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据集的字段\n",
    "job.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 检查字段的取值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "字节跳动              3978\n",
       "阿里巴巴集团            3242\n",
       "深圳市腾讯计算机系统有限公司    2071\n",
       "深圳市乐有家控股集团有限公司    1168\n",
       "贝壳找房（深圳）科技有限公司    1015\n",
       "                  ... \n",
       "张家港康得新光电材料有限公司       1\n",
       "安徽考拉网络科技有限公司         1\n",
       "上海越冠机电设备有限公司         1\n",
       "杭州永续时装有限公司           1\n",
       "湖南迪诺制药股份有限公司         1\n",
       "Name: company, Length: 46811, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看公司名\n",
    "job['company'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "算法工程师                                       1239\n",
       "数据分析师                                       1116\n",
       "Java开发工程师                                   1021\n",
       "嵌入式软件工程师                                     865\n",
       "产品经理                                         852\n",
       "                                            ... \n",
       "数据报告分析（英语)                                     1\n",
       "21届春招-市场调研工程师 (职位编号：TCL010244)                 1\n",
       "商品助理（实习生）                                      1\n",
       "长安软件公司-强化学习算法高级工程师(J13467) (职位编号：J13467)       1\n",
       "初级运营专员                                         1\n",
       "Name: title, Length: 90524, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看岗位名\n",
    "job['title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "深圳-南山区      7238\n",
       "广州-天河区      6162\n",
       "上海-浦东新区     5509\n",
       "上海          5337\n",
       "北京          4640\n",
       "            ... \n",
       "荆州-公安县         1\n",
       "洛阳-宜阳县         1\n",
       "太原-尖草坪区        1\n",
       "青岛-平度市         1\n",
       "呼和浩特-玉泉区       1\n",
       "Name: address, Length: 1013, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看地址\n",
    "job['address'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "本科         79855\n",
       "大专         63179\n",
       "硕士         10403\n",
       "中专          3030\n",
       "高中          2241\n",
       "           ...  \n",
       "招29人           1\n",
       "招150人          1\n",
       "04-09发布        1\n",
       "招51人           1\n",
       "04-11发布        1\n",
       "Name: education, Length: 90, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看学历要求\n",
    "job['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "招1人       66189\n",
       "招若干人      29571\n",
       "招2人       28470\n",
       "招3人       11341\n",
       "招5人        9729\n",
       "          ...  \n",
       "招64人          1\n",
       "招160人         1\n",
       "招42人          1\n",
       "药学,中药学        1\n",
       "招70人          1\n",
       "Name: hiring_num, Length: 146, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看招聘人数\n",
    "job['hiring_num'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-4年经验     47665\n",
       "1年经验       31767\n",
       "2年经验       30343\n",
       "5-7年经验     19661\n",
       "无需经验       17704\n",
       "在校生/应届生    14593\n",
       "8-9年经验      1998\n",
       "10年以上经验     1152\n",
       "本科           322\n",
       "大专           279\n",
       "招若干人         261\n",
       "招1人          229\n",
       "招2人          116\n",
       "硕士           104\n",
       "招3人           60\n",
       "招5人           41\n",
       "招10人          15\n",
       "中专            14\n",
       "博士             8\n",
       "招6人            7\n",
       "招4人            6\n",
       "高中             5\n",
       "招8人            3\n",
       "招30人           3\n",
       "04-02发布        2\n",
       "05-22发布        2\n",
       "中技             2\n",
       "04-24发布        1\n",
       "招7人            1\n",
       "04-27发布        1\n",
       "招15人           1\n",
       "04-29发布        1\n",
       "05-23发布        1\n",
       "05-12发布        1\n",
       "04-20发布        1\n",
       "招20人           1\n",
       "招9人            1\n",
       "05-26发布        1\n",
       "04-19发布        1\n",
       "04-11发布        1\n",
       "Name: experience, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看工作经验要求\n",
    "job['experience'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1-1.5万/月      15954\n",
       "6-8千/月        12727\n",
       "0.8-1万/月      11059\n",
       "1.5-2万/月       8497\n",
       "4.5-6千/月       5373\n",
       "              ...  \n",
       "2.4-3千/月          1\n",
       "10-13万/月          1\n",
       "1-1.9千/月          1\n",
       "32-46万/年          1\n",
       "2.3-2.6万/月        1\n",
       "Name: salary, Length: 1481, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看薪资\n",
    "job['salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "05-28发布             61124\n",
       "05-29发布             60121\n",
       "05-27发布              7420\n",
       "05-26发布              5524\n",
       "05-25发布              2844\n",
       "                    ...  \n",
       "生物科学与技术,医学检验            1\n",
       "信息与计算科学,计算机科学与技术        1\n",
       "08-26发布                 1\n",
       "计算机科学,计算机科学与技术          1\n",
       "测控技术与仪器,电子科学与技术         1\n",
       "Name: time, Length: 173, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看发布时间\n",
    "job['time'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重复值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8305"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看整行重复的行数\n",
    "job.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除重复行\n",
    "job.drop_duplicates(inplace=True)\n",
    "job.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(159365, 16)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看总体情况\n",
    "job.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "address                 11\n",
       "attribute               68\n",
       "company                 31\n",
       "education               24\n",
       "experience              11\n",
       "general_situation       46\n",
       "hiring_num             809\n",
       "industry                33\n",
       "members               8001\n",
       "position             24274\n",
       "record_date              0\n",
       "salary                7984\n",
       "tag                      0\n",
       "time                  6467\n",
       "title                   11\n",
       "welfare              34412\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查缺失值\n",
    "#jobs.info()\n",
    "job.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#缺失值填充\n",
    "# job['salary'].fillna('面谈',inplace=True)\n",
    "# job['experience'].fillna('无需经验',inplace=True)\n",
    "# job['members'].fillna('不详',inplace=True)\n",
    "# job['general_situation'].fillna('不详',inplace=True)\n",
    "# job['position'].fillna('不详',inplace=True)\n",
    "# job['welfare'].fillna('不详',inplace=True)\n",
    "# job['time'].fillna('05-26',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "address              0\n",
       "attribute            0\n",
       "company              0\n",
       "education            0\n",
       "experience           0\n",
       "general_situation    0\n",
       "hiring_num           0\n",
       "industry             0\n",
       "members              0\n",
       "position             0\n",
       "record_date          0\n",
       "salary               0\n",
       "tag                  0\n",
       "time                 0\n",
       "title                0\n",
       "welfare              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除缺失值所在的行\n",
    "job.dropna(inplace=True)\n",
    "#job.info()\n",
    "job.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105977, 16)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看总体情况\n",
    "job.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字段处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['扬州', '广州', '北京', '珠海', '武汉', '佛山', '金华', '成都', '重庆', '上海', '深圳',\n",
       "       '南京', '东莞', '宁波', '青岛', '杭州', '西安', '昆山', '中山', '长沙', '福州', '保定',\n",
       "       '苏州', '昆明', '无锡', '潮州', '揭阳', '海口', '贵阳', '连云港', '江苏省', '常州', '济南',\n",
       "       '合肥', '泉州', '南昌', '大连', '黔东南', '沈阳', '温州', '郑州', '乌鲁木齐', '厦门',\n",
       "       '嘉兴', '九江', '常熟', '芜湖', '石家庄', '梧州', '宁德', '江门', '长春', '桂林', '淮安',\n",
       "       '黄冈', '惠州', '娄底', '洛阳', '汕头', '唐山', '徐州', '镇江', '昭通', '西宁', '台州',\n",
       "       '天津', '湘潭', '眉山', '孝感', '太原', '南宁', '枣庄', '张家港', '清远', '玉林', '太仓',\n",
       "       '盐城', '黔南', '新乡', '广东省', '六安', '铜仁', '南通', '宜昌', '开封', '绍兴', '马鞍山',\n",
       "       '湖州', '襄阳', '兰州', '哈尔滨', '咸阳', '宜春', '赣州', '株洲', '湛江', '廊坊', '漳州',\n",
       "       '济源', '银川', '鄂州', '烟台', '呼和浩特', '安徽省', '南充', '河源', '广西', '泸州',\n",
       "       '宿迁', '肇庆', '义乌', '威海', '浙江省', '衡阳', '岳阳', '湖南省', '海宁', '邯郸',\n",
       "       '乌兰察布', '黄石', '茂名', '锦州', '晋中', '遵义', '泰州', '遂宁', '聊城', '驻马店',\n",
       "       '阜阳', '阳江', '云浮', '丽水', '贵州省', '荆州', '三亚', '丹阳', '陕西省', '潍坊', '乐山',\n",
       "       '江西省', '邵阳', '龙岩', '绥化', '雅安', '曲靖', '北海', '十堰', '安庆', '西昌', '德州',\n",
       "       '宣城', '贺州', '福建省', '汕尾', '宜宾', '洋浦经济开发区', '临沂', '淮北', '衢州', '四川省',\n",
       "       '绵阳', '天门', '海南省', '潜江', '山东省', '自贡', '宿州', '柳州', '滁州', '吉安', '济宁',\n",
       "       '辽宁省', '怀化', '亳州', '咸宁', '吕梁', '池州', '上饶', '广安', '景德镇', '阿克苏',\n",
       "       '普洱', '毕节', '淄博', '周口', '靖江', '随州', '泰安', '舟山', '抚州', '泰兴', '德阳',\n",
       "       '永州', '安康', '新余', '湘西', '漯河', '攀枝花', '荆门', '蚌埠', '安阳', '宝鸡', '保山',\n",
       "       '包头', '常德', '开平', '韶关', '大庆', '仙桃', '汉中', '沧州', '渭南', '郴州', '南阳',\n",
       "       '拉萨', '商丘', '澄迈', '湖北省', '鹰潭', '河池', '红河州', '鞍山', '梅州', '晋城',\n",
       "       '三门峡', '巴中', '信阳', '达州', '广元', '平顶山', '钦州', '南平', '恩施', '临汾', '东营',\n",
       "       '淮南', '琼海', '濮阳', '资阳', '大理', '百色', '焦作', '铜陵', '莆田', '通化', '秦皇岛',\n",
       "       '玉溪', '文山', '延边', '菏泽', '贵港', '西双版纳', '天水', '邢台', '山西省', '燕郊开发区',\n",
       "       '萍乡', '云南省', '西藏', '益阳', '河南省', '石嘴山', '丹东', '黑河', '张家口', '东方',\n",
       "       '铜川', '吐鲁番', '日照', '丽江', '衡水', '儋州', '滨州', '甘肃省', '通辽', '三明',\n",
       "       '防城港', '许昌', '内江', '安顺', '黑龙江省', '吉林省', '内蒙古', '大同', '宁夏', '河北省',\n",
       "       '承德', '来宾', '赤峰', '鹤壁', '邓州', '万宁', '盘锦', '鄂尔多斯', '黄山', '新疆', '德宏',\n",
       "       '吉林', '青海省', '延安', '齐齐哈尔', '白城', '鸡西', '铁岭', '临沧', '海西', '双鸭山',\n",
       "       '阳泉', '长治', '雄安新区', '运城', '呼伦贝尔', '乌海'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统一地址格式\n",
    "job['address']=job['address'].str.split('-',expand=True)[0]\n",
    "job['address'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['本科', '大专', '高中', '博士', '硕士', '中专', '招1人', '中技', '招若干人', '招2人',\n",
       "       '招50人', '初中及以下', '招5人', '招10人', '招3人', '招30人', '招25人', '招6人',\n",
       "       '招100人', '招7人', '招4人', '招8人', '招12人', '招9人', '招99人', '05-17发布',\n",
       "       '招15人', '招18人'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看学历要求的唯一值\n",
    "job['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['本科', '大专', '高中', '博士', '硕士', '中专', '中技', '初中及以下'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去除学历要求中有异常的值\n",
    "job=job[job['education'].str.contains('大专|本科|硕士|中专|高中|中技|博士|初中及以下' )]\n",
    "job['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2年经验', '8-9年经验', '3-4年经验', '5-7年经验', '无需经验', '1年经验', '10年以上经验',\n",
       "       '在校生/应届生'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看经验要求的唯一值\n",
    "job['experience'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['招1人', '招2人', '招3人', '招5人', '招若干人', '招10人', '招4人', '招6人', '招25人',\n",
       "       '招14人', '招13人', '招15人', '招20人', '招8人', '招12人', '招17人', '招30人',\n",
       "       '招7人', '招16人', '招35人', '招9人', '招100人', '招11人', '招18人', '招99人',\n",
       "       '招999人', '招50人', '招200人', '招40人', '招26人', '招19人', '招60人', '招199人',\n",
       "       '招22人', '招21人', '招34人', '招45人', '04-23发布', '05-07发布', '05-19发布',\n",
       "       '05-12发布', '05-23发布', '招37人', '05-05发布', '05-21发布', '05-25发布',\n",
       "       '招66人', '05-26发布', '05-08发布', '05-09发布', '05-14发布', '04-04发布',\n",
       "       '04-12发布', '招70人', '05-17发布', '05-24发布', '03-31发布', '05-20发布',\n",
       "       '04-26发布', '04-16发布', '招27人', '招500人', '招55人', '招300人', '04-18发布',\n",
       "       '04-27发布', '03-30发布', '招24人', '04-19发布', '招80人', '04-28发布',\n",
       "       '04-05发布', '04-13发布', '04-14发布', '招120人'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看招聘人数的唯一值\n",
    "job['hiring_num'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['招1人', '招2人', '招3人', '招5人', '招若干人', '招10人', '招4人', '招6人', '招25人',\n",
       "       '招14人', '招13人', '招15人', '招20人', '招8人', '招12人', '招17人', '招30人',\n",
       "       '招7人', '招16人', '招35人', '招9人', '招100人', '招11人', '招18人', '招99人',\n",
       "       '招999人', '招50人', '招200人', '招40人', '招26人', '招19人', '招60人', '招199人',\n",
       "       '招22人', '招21人', '招34人', '招45人', '招37人', '招66人', '招70人', '招27人',\n",
       "       '招500人', '招55人', '招300人', '招24人', '招80人', '招120人'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去除招聘人数中有异常的值\n",
    "job=job[-job['hiring_num'].str.contains('发布' )]\n",
    "job['hiring_num'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['3-6千/月', '0.8-1万/月', '1-1.5万/月', ..., '5.5-6.5万/月', '18-30万/月',\n",
       "       '11.7-13万/年'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看薪资的唯一值\n",
    "job['salary'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.00-15.00千/月    10694\n",
       "6.00-8.00千/月       8227\n",
       "8.00-10.00千/月      7093\n",
       "15.00-20.00千/月     5435\n",
       "8.00-15.00千/月      3529\n",
       "                  ...  \n",
       "27.08-37.92千/月        1\n",
       "23.33-29.17千/月        1\n",
       "5.83-9.33千/月          1\n",
       "23.00-46.00千/月        1\n",
       "4.30-5.00千/月          1\n",
       "Name: salary, Length: 1250, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#薪资单位不一，统一转换为'千/月'\n",
    "def salary_deal(salary):\n",
    "    low_salary=0\n",
    "    high_salary=0\n",
    "    if '元/小时' in salary:\n",
    "        salary=salary.replace('元/小时','')\n",
    "        low_salary=float(salary)*8*21/1000\n",
    "        high_salary=float(salary)*8*21/1000\n",
    "    elif '元/天' in salary:\n",
    "        salary=salary.replace('元/天','')\n",
    "        low_salary=float(salary)*21/1000\n",
    "        high_salary=float(salary)*21/1000\n",
    "    elif '千/月' in salary:\n",
    "        salary=salary.replace('千/月','')\n",
    "        low_salary=float(salary.split('-')[0])\n",
    "        high_salary=float(salary.split('-')[1])\n",
    "    elif '万/月' in salary:\n",
    "        salary=salary.replace('万/月','')\n",
    "        low_salary=float(salary.split('-')[0])*10\n",
    "        high_salary=float(salary.split('-')[1])*10\n",
    "    elif '万/年' in salary:\n",
    "        salary=salary.replace('万/年','')\n",
    "        low_salary=float(salary.split('-')[0])*10/12\n",
    "        high_salary=float(salary.split('-')[1])*10/12\n",
    "    return '{:.2f}-{:.2f}千/月'.format(low_salary,high_salary)\n",
    "        \n",
    "job['salary']=job['salary'].astype(str).apply(salary_deal)\n",
    "job['salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#添加两个字段：最低薪资、最高薪资\n",
    "job['low_salary']=''\n",
    "job['high_salary']=''\n",
    "for index in job.index:\n",
    "    job['low_salary'][index]=float(job['salary'][index].split('-')[0])\n",
    "    job['high_salary'][index]=float(job['salary'][index].split('-')[1].replace('千/月',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0, 0.83, 0.92, 1.0, 1.05, 1.08, 1.17, 1.2, 1.25, 1.47, 1.5, 1.51,\n",
       "       1.67, 1.68, 1.8, 1.89, 1.9, 2.0, 2.02, 2.08, 2.1, 2.2, 2.3, 2.31,\n",
       "       2.4, 2.5, 2.52, 2.6, 2.62, 2.7, 2.73, 2.77, 2.8, 2.86, 2.9, 2.94,\n",
       "       3.0, 3.02, 3.04, 3.11, 3.15, 3.2, 3.3, 3.33, 3.36, 3.4, 3.5, 3.57,\n",
       "       3.6, 3.67, 3.7, 3.74, 3.78, 3.8, 3.9, 4.0, 4.1, 4.17, 4.2, 4.3,\n",
       "       4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5.0, 5.04, 5.1, 5.2, 5.25, 5.3, 5.33,\n",
       "       5.4, 5.42, 5.46, 5.5, 5.6, 5.7, 5.8, 5.83, 5.88, 5.9, 6.0, 6.08,\n",
       "       6.1, 6.25, 6.3, 6.5, 6.6, 6.67, 6.7, 6.8, 6.9, 7.0, 7.08, 7.3,\n",
       "       7.35, 7.5, 7.9, 8.0, 8.1, 8.17, 8.33, 8.4, 8.5, 8.75, 9.0, 9.17,\n",
       "       9.58, 9.75, 10.0, 10.42, 10.5, 10.83, 11.0, 11.33, 11.67, 12.0,\n",
       "       12.08, 12.5, 12.6, 13.0, 13.33, 13.75, 14.0, 14.08, 14.17, 15.0,\n",
       "       15.83, 16.0, 16.25, 16.67, 16.8, 17.0, 17.33, 17.5, 18.0, 18.33,\n",
       "       18.75, 19.0, 19.17, 20.0, 20.16, 20.83, 21.0, 21.67, 22.0, 22.5,\n",
       "       23.0, 23.33, 24.0, 24.17, 24.92, 25.0, 25.83, 26.0, 26.67, 27.0,\n",
       "       27.08, 28.0, 29.0, 29.17, 30.0, 30.83, 31.0, 31.25, 31.5, 31.67,\n",
       "       32.0, 32.5, 33.0, 33.33, 34.0, 35.0, 36.0, 37.0, 37.5, 38.0, 40.0,\n",
       "       41.58, 41.67, 42.0, 44.17, 45.0, 50.0, 54.17, 55.0, 56.67, 60.0,\n",
       "       66.0, 66.67, 70.0, 75.0, 80.0, 83.33, 84.0, 85.0, 90.0, 100.0,\n",
       "       120.0, 130.0, 150.0, 166.67, 168.0, 170.0, 180.0, 200.0, 240.0,\n",
       "       250.0, 300.0, 350.0], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看最低薪资的唯一值\n",
    "job['low_salary'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0, 1.0, 1.05, 1.25, 1.47, 1.5, 1.51, 1.67, 1.68, 1.89, 1.9, 2.0,\n",
       "       2.02, 2.08, 2.1, 2.2, 2.31, 2.5, 2.52, 2.6, 2.62, 2.73, 2.77, 2.8,\n",
       "       2.86, 2.92, 2.94, 3.0, 3.02, 3.04, 3.11, 3.15, 3.2, 3.3, 3.33,\n",
       "       3.36, 3.5, 3.57, 3.6, 3.67, 3.7, 3.74, 3.78, 3.8, 4.0, 4.1, 4.17,\n",
       "       4.2, 4.3, 4.4, 4.5, 4.7, 4.8, 4.9, 5.0, 5.04, 5.1, 5.2, 5.25, 5.3,\n",
       "       5.42, 5.46, 5.5, 5.6, 5.7, 5.8, 5.83, 5.88, 5.9, 6.0, 6.1, 6.2,\n",
       "       6.3, 6.5, 6.6, 6.67, 6.8, 6.9, 7.0, 7.08, 7.1, 7.2, 7.35, 7.5, 7.6,\n",
       "       7.7, 7.8, 7.9, 8.0, 8.1, 8.2, 8.3, 8.33, 8.4, 8.5, 8.6, 8.67, 8.75,\n",
       "       8.8, 8.9, 9.0, 9.17, 9.33, 9.4, 9.5, 9.6, 9.8, 9.9, 10.0, 10.42,\n",
       "       10.5, 10.83, 11.0, 11.25, 11.67, 12.0, 12.5, 12.6, 13.0, 13.33,\n",
       "       14.0, 14.17, 15.0, 15.83, 16.0, 16.25, 16.5, 16.67, 16.8, 17.0,\n",
       "       17.5, 18.0, 18.33, 18.75, 19.0, 19.17, 19.5, 20.0, 20.16, 20.83,\n",
       "       21.0, 21.67, 22.0, 22.5, 23.0, 23.33, 24.0, 24.17, 25.0, 25.83,\n",
       "       26.0, 26.67, 27.0, 27.08, 27.5, 28.0, 28.33, 29.0, 29.17, 29.25,\n",
       "       30.0, 30.83, 31.0, 31.25, 31.5, 31.67, 32.0, 32.5, 33.0, 33.33,\n",
       "       34.0, 35.0, 35.83, 36.0, 36.67, 37.0, 37.33, 37.5, 37.92, 38.0,\n",
       "       38.33, 39.0, 40.0, 40.83, 41.0, 41.67, 42.0, 43.33, 43.75, 44.17,\n",
       "       45.0, 45.83, 46.0, 46.67, 48.0, 49.0, 50.0, 52.5, 54.17, 55.0,\n",
       "       56.25, 58.0, 58.33, 60.0, 62.5, 65.0, 66.0, 66.67, 70.0, 70.83,\n",
       "       75.0, 79.0, 79.17, 80.0, 83.33, 84.0, 90.0, 100.0, 120.0, 125.0,\n",
       "       130.0, 140.0, 141.67, 150.0, 160.0, 166.67, 168.0, 180.0, 200.0,\n",
       "       250.0, 280.0, 300.0, 350.0, 400.0, 416.67, 450.0, 500.0, 600.0,\n",
       "       700.0], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看最高薪资的唯一值\n",
    "job['high_salary'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['数据分析专员', '商品数据分析师', '数据分析师', ..., '咨询师（松鼠Ai智适应教育）提成不封顶',\n",
       "       '结构工程师储干', '南山地铁站底薪7500销售储备干部'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看岗位名称的唯一值\n",
    "job['title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5                  数据分析师\n",
       "6             领军人才—数据分析师\n",
       "7                  数据分析师\n",
       "9         数据分析师/主管（零售方向）\n",
       "11               数据分析工程师\n",
       "               ...      \n",
       "167627        Java讲师应届硕士\n",
       "167657      XY101-博士后研究员\n",
       "167658               学管师\n",
       "167662           结构工程师储干\n",
       "167663              储备干部\n",
       "Name: title, Length: 50314, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去除非技术岗位\n",
    "job=job[-job['title'].str.contains('商务|电气|策划|客户|会计|商品|招聘|产品|销售|市场|翻译|文职|顾问|行政|客服|编辑|实习|专员|助教|助理|人事|咨询|财务|运营|文案|教师|审核|管理|采购|供应|贸易|人员|设计|助理|老师|毕业生|营销')]\n",
    "job['title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50314, 18)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同岗位数量在全国范围内所占的比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "算法      23523\n",
       "数据分析    19506\n",
       "数据挖掘     3406\n",
       "人工智能     2935\n",
       "机器学习      645\n",
       "深度学习      299\n",
       "Name: tag, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同岗位的数量\n",
    "job['tag'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4TFVv7s5gN4YGWRhjLf2MKuFbtk+sPGjx0Dz15HsaswtaG7MGt7Vk5UrYF8iIeDP8EEG1NQZhVQ3HVMOgbYKGBdo8aMSDRnwQ8aLRDIj60JgfNBNifkH9fIrMVo8tqsqeNLyobcrI4rZFNesz26IxOWr8mNaObV5Zsdr/3+o1WZk+rw7PGxT94uEHN97y3Kv5V558bL3P6+HRRVXZE4sL25Zu2JJZUpAXbWhulaPGjWkpyAmo1xOfpFuKbL72oSem9La9iIwHfoeprHwk8AZwCjAJ88DegImwbex4GebftlVVz3L6CQC3AN8BDgFiQBmwTVV/v9c9c4Cmjp6IiJwFTFfVUHf2uuERdOv+WPYfETLnFlcfd/q85t3nv7huzWmL9DAPFLZ/rqDhzPztTdlF2xqzRzTszilpbcoZIy3+If62zEF5UU9mMSI9VgFWjbSgbS3Q1qzaFkbDrarhNpxDNRxFwxHVVoU2R2zMsbv1g4OQQVtvee614EmlpRu27qodJGTWQSwDjOAMz8vNGOT3i8/r8VZv3eFbumGLf3JJMb/690tDABpbw5x5cGnWso1beXbJSk6dMpE/L3w7cPahU5hQVNiT+fv3b4vu7lU7kTOB44EfApcCfwauAHZghOA/qrpaRCLA5ZiXYkhV54nIycAc4KdOX5OB54CVwDMdbuMFoiJyHXCaqi5xzn8J+JqIXKKqy51zmfTixZt4IQjVxwjlR7FDhLgwNBYr/O32bZsvOaXo6AfnEJ7/dGzBrKU6QyBfQPzh+mH+cP2wgp0rO70+6slsag4Ubm7MGVG3O6ekqTF7eLQ5MCwj7M/PjXgDQ1Q8w0V8WYgvCwKD+zrZkzdoPQs+rC4W71Ayc06dlNO2lqyCr+6xSrJj/aN85shzOGjkzNivH7+CZdvy2kqGTG05ZFytbqit8Xl2bfNtaZmyJsKWIYeOHdla16I6LK8k482aXbEDR85aAWFRbRPj6US8EPGoRnwQzTBeTizTeDiahfFwsoGMHkxv6833U9UnRCQbmAUMA76LEeNLgW/wSWUvD/BFTEr/SSLyuNOuEPPmPxOIAH8BrgOie885iMj/0OHFqqr3iMhaoFJEZqvqBowQ7OFxdYZbcQStmH98Sxw4qqV16tyGXS/fl583+7bPe+fcfYrWXfFE7KXpq/QIgUB313pj4ezcxo3jchs3UsyifT5XJNbqH7y5Kbt42+6ckt2NOSPCTdnF0uIvCLRl5ObHPBnFiHQZOTqqcAKDcwr5cMO7bNhRTdHg0fu0WbX5AyaWTEc15tnesJHDJpzoz8nK8zc0N7GruZHcwFCGFx49KeBfSjQWJRqDSaNnsrV+A5m5Zfu19Koai0BbM9rWqtrWgra1ouGw0u7ltK3pfV/6kIgI8GPMwz8UKMLMD/hEZCdma/MfgXuB/wPeBkqAVar6K6efFcAPRORnwDGmS7IwQ40HVfWrndz7eRGZCdSLyBhMifekFYIwVgjiyrW1O2cvDAQWVmdmzNqdLQU//7L3+KENuvmqf0TfmriRo6XnN2CnCOrJaq0bntVaN3xI3fJO20S8WQ1NgWFbG3NG1DXmlLQ0Zg+PNQcKfeHM/LzDxs0uvPmJa0Y0tjawq7mOi0++nifevJszj/jax9d/ZuaF/Pn5n9Ha1kKmL4s5075A7a4tPPvugxQNHkWOP58cfx7jhk/j5Q/+yWETTuTJt//MSQd/af/+sQARjw/8gxD/oC68nF4nJRERP7BFVQeLyCzgUYwH0KKqZztt7gSexUwsrsUMEzKcz0aq6lUi4gG+CqwDTgTew8wRfB74ozMPsVpVYx0mC1sxnkgJsAkz7/AW8IdubXYpoGgjMCLxN04vmkWajhszckOrxzOx4/mSHbrm6sei60dt52hxIaX9zmgset7atfqbqSesyBkyfmdjTkm4KbvI2+IvCER82YNjnozhja27cn9X+QMuOTXEkEHF/GXB/+OcY75FIDOH5xf/Hb8vwLGTz2D1lqVsb9jEruY6tjds5EvHfideZj93+R0nntpTIxHxAl/DzBFMBf6N8QjeAc4GZqjqShHZ4LQLAlcCa4AaYCfgU9VrReQw4DanzUrMAz4M2I0ZqmwGrlXVN0RkCnA1ppbo3cBdqlorIncBBap6Tnd2u+URbMEKQdwJqGY/vHFz5udHjqjv6K5vHCoHXH2J74Dxm3Tl1Y9F6wobOCKRdg32erzFXmFy4+rJNK7e5/OwKt9YvyFyRcmBmw7d8NymxpySloba6skb1y7cPH7MMVRvqpowZfQRfkC21W+gKH8UzeHdxPmltqOX7c7AzPJ7MG/6Csx4fwPwEfCQiHwb83YPYJ6FtzAiMRYzVHjS6asJOABY0eH3NmA9ZolwIp+sOjQ7/e0A/qWqtc75ot7Y7pYQbCQNMsMmA+PaIgfctL32jR8XDjkC2XNPQ/UImfity31MWx2ruvKfsWhec+L/n3zU2kplQwNXDhv28bnHdu5kRUuT7+8blo3++4Zlo88bXMCNgQyue+Xmwo1tv2R6IMB3M3eEm3a+sXVnWFoP1Gnb/vruI9M+O/XMNZnhhpY2X6BAxVeMmbTrL7b3st1S4J+Ycfx84L+Y5b4AJvdhGabcehFwM2Y4UIDxBrzAVZh5ge8Cr/HJZOHpmNWHZ1T1OthzslBVVwHni8hJmMnCG4EQoMA9PRnt1tDgLsz6qiVBXF48bMHL2YE53bU5Zmls0aVPxbIDbZQmyKy4Es4YtMNZKt25O7ck3JRdrC1ZQ/3hjEF5UW9mEeLpy1rjjZffceJPe9NQRO4HjgOWYNz/36jqBSLyuqoeJSK5GI+4FLOKsBAzfDgIWIB541er6jqnv58Bs/nkxX0kcKeqXtrF/QcD9cBozJJlpqp2O0fgpkdgSSC3bdl23AljRr5b5/V2WU/itcmema+VCqe+o6/Pez5WlBFlXCJt7G8y23YNzazfNXRwfXWnn0c9vpaWrPal0hGNjdkjIs2BIl+rPz8n4gsMUfEOx0z8gXk4e0REhmMS72zGTBJOY1/X/EBMrMAujAi8gdnmHAHOwbzlr3MmC6WDB1AK3AWcBjzvzEeoqsY63D8b+ArGG7kLM4TocdXALY/gMnqYxbT0Pzs8nu0njhnZFhPpcX7GE9PoFxbqf89ZGBvrVUb21H4g0iEAa2uLv+DyE1+696WerhGRKzBzAz903v5zAb+q3tnuETjtfg/ch5kDGIwJQroRGAmUqer3ncnCW/hkxSIXsxrQPmfgYc/JwisxnkPHycIrMVGID3Rnt/UI0oihsVjhHzdvXXLJ8KKhiHRbXCbmEe8jx8mxjx8j4Qufj7102js62aMM6+6aeLAzGmVpSwulfj8Fvj3/XLdHIuR7vWTEKZ1DxwAsel+H43eqqiJyvfNGvwjzhgYQERkHHI5ZBvwNZu7gdszDfhcmTmABgKq+jQk4ar94OnCZql7WyX2bgWXAlaraMZJwFGZvQre4VQ25R8Ms8cEJNnq9t+0jXsm851Tv8Rd9zxt4ZbK8pGbs2SciqpxY/RHz1q5h3to1rGht2adNfTTKpevX8cWaGm7avPnjc99av57FLc1ctG4dtZEIf62r40tramiKxVjY2Bg3Edj7K9BLIegQ/ZeDWe9fqKqbnXPrgb9jPAAf8AXMBGAmxmuYA5xHJzEeInIG8Au6WAFQ1VWqenO7CIjI6SKyErOcuLAnu90aGvgwyx6fuuSZZf84a+SIhdWZGbP6el1uk9Zd8UTs/emr9MieohTbWdrSwtO7Grh6WFGXbe6vq2Ww18uZeflcu3Ej84YU0BxTMkU4JBDgl1u3ckxONs/s2sXUrCzGZmayMxrl1EF5ff0K+8Oq0uXLxu/PhSIinW1HdoKOwp2EDQuQsfeWZDF7QIYDyzvOCfQX7ngEofoI0HlYmiUhPLhx8wx/LNb5hoNucKIU53zrcm/9ihJe0V7E4L/f3MyC3bv58poart+8iUgnL5/BXi8rW1tpiEbZFGljuC+Dw7OzOSQQ4O2mJqpampmeFUCBiMLCxiaOy8ntq/n7y37/rXaVk0BVWzv7TA37bMxT1VpVXRoPEQD3hgYAi128d9rTHmyEap9dfYAdeTL8+nm+466a7924rpCFasa8nTI1K4u7R4/hoQOCRFR5uXHfjXyHBrLZ1BbhL3V1jM/MJN9r9qSpKk/vaiDP68UnwqzsHF7avZvhPh+Xb1jPG02N+/QVB95MxE3cxApBGuMEGy3/NCF5TpTirB9d5K3eltf5AzPJ72eYM9E3JSuLNeF9d6L/fvt2biou5luFhYzN9POPeqNPIsINxcOZ5Pfz4u7dfCYvj8sLCxnk9XB8Ti7P7dq1v6b3BSsEcaTKxXtbHM7d3Xjk7OaWHpfFeqJ6hEy8/HLfET89z1PVEOC9jp/9YPMmlre0EFXlhd27meTfN5tQQyzKitZWoqosbmlGgLt27OCfjiA0RKMM8po/1zXhMKMzMskU6doN6V8GvBC4M1kIEMofjtkdZXGZKERPGDNycXfBRn2lY5TiytZWrt24EQVOyM3ljLy8fcKKFzc3c93mTWxsa2N6IMBtI0cRUeV7GzcQVmWi388NRcU0xmIsbmnhkEAWF6xdy7eGFnLKoLimbqwuXb5sQjxvkAy4JwQAofwVmI0TFpfpS7BRr1EdCFGKD5QuX7bPvv+BhptDA4AXXL6/xcEJNtpBD5l0+4QIz870HHXhNd4DHj7O82rU07sw3SRjwA8LwAqBpQN9DTbqLTGPeB851nPshdd4i56eKS/FhG39fY848obbBiQCt4cGwzD7sW2dgyRif4ONeos/rI3z/x1769gPTC7FeN2nH9gFFJYuXzbgE+66KwQAofzFmB1aliShq8xG/c3+RCkmmMdKly/rNrPPQMHtoQHA824bYNmTTxts1FvaoxS/ebm34cORvNybKMUEU9nXC0Qk4GwP3m+c7ca9addvnnQyCMGTPTexJJr+CDbqLbV5UnzDXN9sJ0rxte6iFBOIAk/tx3U3ABfvfbIrcRARj4j4Ovw+C7jT2TT0WREZ5/x8ulP3oL2dH3jdyX+AiJwvIl8XkSEicm5fjU4GIViAmSewJBn9FWzUW5woxWO6i1JMIK+XLl+2uedm+xDBJBfdm7tE5CURWSAiq0VkuYgsAF4GfglGFDAJS/4A/ApT46AIk6dgMCaRaTtXO20PE5HvYLyprzh97eyr0e7PEQCE8n+HSedsSTLiEWzUW9zMpQhcU7p82a/7epGI/AZYpqp3dtPmemC9qv55r/OHYkRgF6bIyXuYVYsJqnpeh1RnB2NeoDWYLcujMAlM1qnq3SJyMWb78wp6STJ4BAAPuW2ApXO84P3H+k2jPaoJjwKtGuuZ9o3v+qbffJZnUXMmSxN8+0f287rJmJyCfUZV31HV9jqJ6zAZjP4EjBeRyzBJTsFkKDoRU0x1GjADeBG4xElo+j32rKnYI8kiBK9i0j1bkpC4BBv1QKQ+gkaMt/rfUs/MeVf7Jt91quf1sDchSW3eKl2+rNeVjdoRkzJ+MDBSRLpOvtD19V4ReRjzwM/CpC0bikle8h5wAYCqtmDSnj8mIv/BiEUd8GuMgJ3jlDvr/b2TYmgAEMq/GVMnzpKk/L8hg1++Lz9vdmefbbxvI7nTcsmbsW+ikB0v7KD+DbMAEWuKERgfIGtM1r7nRmWxc+FOxpaPpf7tegpmFezTlyem0bNf0/+euzAW9MYY1Z/frwPzS5cv69K17woRuQGoxbzUPqeqX+uiXadDA+ezGZgUZhnsWV3pQFUt6dDuVZyUZpjSAPdiyqo3A3WqelOfbE8iITgUOim2Z0kqOgs2avywkR3P7mDMFWN6vH7j/RspOLaAwNjAPudqX6wlMDZA5vBMoruj5B/edayRL6rhC16IvX76Ii3t51yK9UBJ6fJlTX25SESOwUzwHaeqURH5J/CCqt7aSdsuhcD5/FngCfasYnyVqk7p0OYtPkkb14KpujwJ+Pn+JC9JlqEBhOrfwVR8sSQxe2c20oiy4Z4NZBRm0PBOQ7fXttW1EWmI7CECHc+pKhpVdn+wm0EHd7+jMOKVzD+f4p0973ve7FemyIL9yaXYBffuhwicB/weOFdVo87pC4Evi8j9InKTiLwuIq86b/KLMUVMXnWON0Xk2g5dKmYo8HaHY++U5FtU9WRM3YJ2Njt1EK8QkVPoA8njEQCE8i+iF1VZLO6yKsO35vMjRwxGJL/2pVp2L97NiAtHUPufWnz5PoaeMrTT6zY/spncKbnkluZ2eq7+jXrqXqlj0IxBNCxqYNiZw/Zo2x25zbrz20/E3ptR/amjFA8qXb7sw942FpGDMBWFLlfVHXt9lgX8CHhIVT/oQ58vYF7SHYcGk/caGjyjqqeJyDXAeMxz8yBmknEUcHxf5gmSTQiyMF+kLxVoLC7wSG7OGz8uHHLExr9skkGHDGLQwYNo2djC1ke3djpE0Jiy6merGHf9uI8rr3V2rumjJsLbwkQaIoS3him5sG9Vzoc06JarHo9+eOCG/ar4/ELp8mUn9fEaV+kqOWpfSZ6hAUCovgW4w20zLD3THmyUWZRJeJtZTGhe3UzG0M6fvaYVTWSPy96j/GJn51q3tJJZlIn4xDjIfaQ9SvG7872besql2Am/7/sd3aU/RACSTQgMt+MUdrQkN7dt2Xbc2GPy3m9c1siq/11F7Qu15B2ex5ZH9w0U3bVkF9mTsrs9F22OkpGfgb/ET92COnIm5+y3bZuGypiecinuxQZM8dK0JLmGBu3YIqkpQ1wyG8WBaatjS678Z6wtr5muIiT3K5JwoJCsQjAGWIktgJISvJ7lX3LJ8KIDeyqjlgwcvSy26LKnYoFAmMkdTm8GxpUuX9bc1XUDnWQcGkCofi3wR7fNsPSOeGU2igftUYp3nrZHlOIv0lkEIFk9AoBQfjGwCsjuqWmiqG1WFm2MMmOEh8Ls+Gnolt0xhgSEDG9qJW6Kd2aj/sYT0+gXX4k9fc5r+sXS5cv2LciYRiSvEACE8n8OlLttBkBds1L2QBNlE3387YM2XpibzbzHW9jaGGPmCC9/PLPzpes/vBXmoQ9Mvo2dLcqRI71MH+7d59y0Yi/3vh9mwbwcHl3WxtxDkt7L3odEZTbqZ75eNa/qbreNcJtkF4ICjFcw2GVLeKkmgt8HR43ycc2zLYzOEwqzPXz14Ay+8mgT3zvaz2El3SemueKpZuZNz9yjXfu5O94Oc3iJl0mFHnY0KedM7usSeHJQneGrOWvkiALMBpxkZwlwSNW8qmRIhOIqyTlH0E6ovg74mdtmABwf9HHUKB8vr4nw5oYog7OEJVuj7GxR1jUoo/O6d+M3NMTY0qh7iEDHc6rQFoNnqyN8ZqKvm56Sm/FtkWCiMhv1A9+3ImBIbiEw3EKS1ElUVR5a0kZBQJgT9LGmPsZtb4QpLfQwJNC9ENz+VphvHpbZ5blTx/t4ckUbo/I8fO7BJl5cHemsm5Qg0ZmN9pP/VM2retptI5KF5BcCU0L9UpIgj52IcHtZgIOLPMy5t5E7zghw4/F+Dir0cM97XefdjKnyYk2UOUFvl+e+PDWD0Bw/g7OEsok+Hl2WbHk8+8ZtW7YdVxCNvuu2HV3QCMx324hkIvmFACBU/zouLyf+4tVW7nvfBDzubFF2tihVW6JEY8obG6LdFmZ4ZU2UI0d69wil7ezcyh0xxhcIfp8QSwXHuhvczGzUC75fNa9qtdtGJBOpIQSGH2ICP1xh/sxM7l/cxux7Gokq/Pur2cx/soX8il3UNivnT8tg6bYo17+w7yrUM9URZh/g7fZcQ6syPNfD5GFe/rQozMnjUneeoB03Mhv1gucxeQEtHUjuVYO9CeV/CZvfMOXoLrNRgtkFTKuaV9XnNGQDnVTyCCBU/zDwgNtmWPrGtbU7Z48Pty102w7gGisCnZNaQmC4DBKSwNLSj+yd2cgFnquaV/UnF++f1KSeEITqdwFfxm5VTikCqtkPbdycEe8yal3QgN3N2i2pJwQAofpFJEnosaX3uBhs9K2qeVXrEnzPlCI1hQAgVH8ztm5iyuFCsNHNVfOq/prA+6UkqSsEhnnAR24bYekbCQw2+g9wbY+tLCm2fNgZofyJwOvAELdNsfSeBGQ2Wg0cVjWvqjZO/Q8oUt0jgFD9SuBs7ORhShHnYKOdQJkVgd6T+kIAEKp/GTsrnHLEKbNRGPhC1byqZf3cb58Rkf3PvppgUn9o0JFQfgjoU803i/v0c2ajuVXzqu7vTUMR+QmmivDJmKjD2zFFRD/bXrFIRHzAw6r6BRF5AOhYaGG3qp7htDsZOAxTiLQ9F8MrwHEd2rY47aY791oLvN+hv6nAlL0LpSSCgeERtBOqD2FzHaYc/RhsVN4HEcjFxBccDRQBE4EDgEandqFHRLyqGuGTJLoZqjqn/cDUHEREMjBVGKIYUXkcuAaTHv0aTBWiuSLiddqEgTZgKabwb/vxLvuWNksIqb+zZV++CQh2m2nK0B5sdNbIEfWfIrPR96rmVd3ch/b5mJLj38bUGXzN+XmCiLwMTABuFZETgeki8ggwWkQWAF7Mg99ejfkKzArWIOAxoBKYjImC/TlwH6Y24+nA/wK5QA0wxmnTTjVGKBLOwBoatBPKF0zFJCsGKUR7GbU99mb3jAKXV82r6tOOQhEZDtwMfIipIhwGDgZuxCxJX6qq33XaPokpVT5fVc8TkfMBVPXBDv3NwQwDbgfuBm5R1QUichRGYOY6BUrnYIYAvwcexlQ9rgYOwojFPxwvJKEMrKFBO6F6xSitjS1PIfYj2CiGST66P9uKfcCPMfURf4MRhU3ATMybehWAiByPecDHAQeJyH8wW+J/KCILRGS+iBwAXAR8AVO3Mwpc77T9H+BpRwQKgcOBrwFfxQwtyhxbsjBVkl1JwDMwPYJ2rGeQckQhesKYkYvrvN6uKhK1E8FMDD7YQ7tOEZFZmId0ImZoMB0Yi3HtFwFPYzyFcwGPqp4hIver6oUici6Aqj7i9HUGMAdTlvxXIpKhqm0iMh84Dfi2qm4SkQswYvEqZpLyAqAYKAVygPXAZ1Q14empBrYQtGNXE1KKXgQbhYHzq+ZVPfZp7iMi3wSOwjz0M1T1ByLyY+DzwKGAH/PWfgL4FnAV8DlMxW6Aiao60ulrDnCYqv7K+f1GTHn2H3UsVNphaBDGzFMMd/qf6jR5U1V7U6uxXxmYQ4O9MasJc7FBRylBD8FGLcDZn1YEHO7AZCy6HpgqImOBKZiZ+0NVtdl5iBU4AXgBeKvDqsErnXUqInOBTFX9oaqqiJwtIseLmfvId/o717n30A6XFuJSmb/0EAKAUP39wCmAjTZLAboINloHHFs1r+qpT9u/iBQAD2Im6Y7CzBfcA1RgHtLbRWS8iPwCeBaYDfwbOMKZG1gAHO/0NRz4LWbiEUx1rpNF5HERedzpcwcQBL6PCX9uBdY450LOPS/CeBEJJz2GBh0J5R+IWd6Z4LYplp7pEGz0CnBu1byqrf3Vt4j42mfonbe1p0MgkWgnD0f7+L+357u5d6f9u0X6CQFAKH8o8GfgDJctsfRAs0jTZ0eV/Ha7z3tD1byq1M7xnsSkpxC0E8q/CuO2pV6hwfRgN3ApoXqbpzLOpLcQAITyZwJ/ww4Vko0q4EuE6pe7bUg6kD6ThV1h0p4dis2OnCxEMOv7h1kRSBzWI+hIKP884FbMJhRL4nkfuJhQfbKWShuwWI+gI6H6v2GivO5125Q0ow2zhHa4FQF3sB5BV4Ty52A2kEx22ZKBzsvAFYTqk6LidbpihaA7QvkZwJXAdcBgd40ZcKwAvk+o/p9uG2KxQtA7QvkFmGy438FsDrHsP9uBnwB3EKq3cQFJghWCvhDKLwZ+BFyK2ZBi6T27MXvw/5dQvRvVjizdkFAh6M8ccR36vMjp41hAVfWZTu77CnCqqjaLyJmY+O6DMDu/5qpqY5++SCh/NHADZiOTFYTu2YKJw/89ofo6t42xdE7CUpV1kiNuOHvliMMIU0RE9sgR16GPR/bq8wBMmqh7MfnenhSRl1S1PZecB5N4IgxEnZxxN2HSmf1VVfdvA1Kofh0wn1D+dZhcB98ERu5XXwOXlZhEnvcSqm9x2xhL9yTMIxCRkZiUTR1zxOUDx2Dy0E/ArOGfiEkS8QowGmimQ444VR3Xoc9/Abe3ewEiciVml9h5TmKIKcDVwBeBh4A7gQpVPaFfv1wo34dJOPEdoL+y8aYiUeAZ4P+AxwnVu5Jtx9J3EikE/Z0j7ipgBjBvr8QPv8Rki/mGqi4WkXFO/0Wqul1ElmASRxZg1q9fVNUf99sXDeXPAC4EvkT6eAlLMF7ZXwnVb3LbGEvfSaQQjMJkb70Qk+65DZPR9U3gv8AkVb3NyRH3L8yb/NuYWeb2SL9aTCjwM8DvMEtQp2MSOqzGeA60f6aq/xWR2zA54l7EVFC+RVVPEZHrgVdVdUFcvrBJkzYLU8L9i5iUVAOJdZj/j/c6YdqWFCaRQtBvOeL26vcrwARV/YkzZ/AbVT3H+awUk2xiN5/EAlylqqfHXQg6Esr3YJJYnAmcBEzDpFxPJSKY4dxTwFOE6qtctsfSjyRsslBVF4rIw3ySI+4DZxz/DnAWZm15MXA/8ISIjAG2i0g1To44Ebm1PUdcB84FfuH8XMIn+eTAZIy9Gvixqr4hIrMxbmxiMWPlF50DQvmFmNRXJzrHgQm3qWfaMDsA38Kk1HrWLvsNXBJd4OQOoBGTI25NJzni3gYQkY454kao6nnO+b917ExEvgNEVPUN59SBdBACVb3Padf+9r0Ks2TpLqH67cDfnaM9PmE6xlM42DlKSVyehFZMbv23MQ/+W8B7hOpdqbpjSTyJXD4sAP6AyRd/FCaG/x5MSagtwCOOmz+fT3LEXYapNrPA6WaS05dghhQ7MCmhEZGbMDkJ53Zye7+ITMPMITzvnMsiWWIAQvVbMPMen8RAmJWISZh8+iXOMaLDz8WY/HaZzpHRSc9hjPDuBOqcYz1mPqX9WAVsdGpBWNKURAcU9WeOuJzeBgKJSL6qDmy31kxOtosCQBOhelfKZ1lSDxtibLFYbD4Ci8VihcBisWCFwGKxYIXAYrFghcBisWCFwGKxYIXAYrFghcBisWCFwGKxYIXAYrFghcBisWCFwGKxYIXAYrFghcBisWCFwGKxYIXAYrFghcBisWCFwGKxYIXAYrFghcBisWCFwGKxYIXAYrFghcBisWCFwGKxYIXAYrFghcBisWCFwGKxAP8fqa3EfCrg6Q8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#解决中文显示不正常的问题\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif']=['SimHei']\n",
    "\n",
    "#绘制饼状图\n",
    "plt.pie(job['tag'].value_counts(),autopct='%.2f%%',labels=job['tag'].value_counts().index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，算法和数据分析岗位的需求量最大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 每个岗位薪酬和工作经验间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-4年经验     17532\n",
       "2年经验       10330\n",
       "1年经验        8249\n",
       "5-7年经验      7286\n",
       "无需经验        3423\n",
       "在校生/应届生     2247\n",
       "8-9年经验       786\n",
       "10年以上经验      461\n",
       "Name: experience, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同经验要求的岗位数量\n",
    "job['experience'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱状图\n",
    "job['experience'].value_counts().plot(kind='bar',title='不同经验要求的岗位数量',color='blue',alpha=0.5)\n",
    "plt.xlabel('经验要求')\n",
    "plt.ylabel('岗位数量')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，需要有3-4年工作经验的岗位数量是最多的，说明公司更想招有经验的员工，但一般7年工作经验或以上的人已经发展到一定高度，很少有跳槽重新开始的，所有岗位数量少，有1-2年经验的，公司也乐意接受，而在校生或没有经验的相对来说就处于劣势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制折线图（工作经验-平均薪资关系图）\n",
    "job['low_salary']=job['low_salary'].astype(float)\n",
    "job['high_salary']=job['high_salary'].astype(float)\n",
    "y1=job['low_salary'].groupby(job['experience']).mean().sort_values(ascending=False)\n",
    "y2=job['high_salary'].groupby(job['experience']).mean().sort_values(ascending=False)\n",
    "x=job['high_salary'].groupby(job['experience']).mean().sort_values(ascending=False).index\n",
    "plt.plot(x,y1,label='最低薪资平均值',color='blue')#标签、颜色\n",
    "plt.plot(x,y2,label='最高薪资平均值',color='red')\n",
    "plt.title('工作经验-平均薪资关系图')#标题\n",
    "plt.xlabel('工作经验')#x轴\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('平均薪资')#y轴\n",
    "plt.grid(alpha=0.5,linestyle='-.')#设置网格、透明度\n",
    "plt.legend(loc=1)#图例展示位置，数字代表第几象限\n",
    "plt.show()#显示图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，工作经验与薪资存在正比关系，经验越丰富，薪资越高，并且经验越高，最高薪资与最低薪资的差距会越来越大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 全国不同城市，针对3种最热门岗位的岗位需求量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "算法工程师                843\n",
       "数据分析师                779\n",
       "Java开发工程师            691\n",
       "嵌入式软件工程师             623\n",
       "软件工程师                555\n",
       "                    ... \n",
       "电话催收操作工                1\n",
       "C++开发工程师（集团研究院）        1\n",
       "建模及算法工程师（智能网联）         1\n",
       "专利信息经理                 1\n",
       "品质主管（包住，有餐补，广州上班）      1\n",
       "Name: title, Length: 26868, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#各个岗位的岗位需求量\n",
    "job['title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['数据分析师', 'Java开发工程师', '算法工程师'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#最热门岗位：算法工程师、数据分析师、Java开发工程师 \n",
    "job1=job.query('title==\"算法工程师\" or title==\"数据分析师\" or title==\"Java开发工程师\"')\n",
    "job1['title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "address  title    \n",
       "上海       数据分析师        133\n",
       "         算法工程师        115\n",
       "         Java开发工程师    107\n",
       "上饶       算法工程师          1\n",
       "东莞       数据分析师         20\n",
       "                     ... \n",
       "青岛       数据分析师          3\n",
       "鞍山       算法工程师          1\n",
       "马鞍山      Java开发工程师      1\n",
       "         数据分析师          1\n",
       "黄石       数据分析师          1\n",
       "Name: title, Length: 194, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3种最热门岗位的岗位需求量\n",
    "job1['title'].groupby(job1['address']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>title</th>\n",
       "      <th>Java开发工程师</th>\n",
       "      <th>数据分析师</th>\n",
       "      <th>算法工程师</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>address</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>107.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上饶</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东莞</th>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>东营</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中山</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青岛</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>鞍山</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马鞍山</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黄石</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title    Java开发工程师  数据分析师  算法工程师\n",
       "address                         \n",
       "上海           107.0  133.0  115.0\n",
       "上饶             0.0    0.0    1.0\n",
       "东莞             7.0   20.0    9.0\n",
       "东营             0.0    0.0    1.0\n",
       "中山             2.0    3.0    3.0\n",
       "...            ...    ...    ...\n",
       "陕西省            0.0    1.0    0.0\n",
       "青岛             5.0    3.0    4.0\n",
       "鞍山             0.0    0.0    1.0\n",
       "马鞍山            1.0    1.0    0.0\n",
       "黄石             0.0    1.0    0.0\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3种最热门岗位的岗位需求量\n",
    "job1['title'].groupby(job1['address']).value_counts().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同规模的公司，对于人工智能岗位的薪酬待遇 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>attribute</th>\n",
       "      <th>company</th>\n",
       "      <th>education</th>\n",
       "      <th>experience</th>\n",
       "      <th>general_situation</th>\n",
       "      <th>hiring_num</th>\n",
       "      <th>industry</th>\n",
       "      <th>members</th>\n",
       "      <th>position</th>\n",
       "      <th>record_date</th>\n",
       "      <th>salary</th>\n",
       "      <th>tag</th>\n",
       "      <th>time</th>\n",
       "      <th>title</th>\n",
       "      <th>welfare</th>\n",
       "      <th>low_salary</th>\n",
       "      <th>high_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>155859</th>\n",
       "      <td>佛山</td>\n",
       "      <td>外资（非欧美）</td>\n",
       "      <td>毕马威全球商务服务（广东）有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>无需经验</td>\n",
       "      <td>Overview,KPMG China operates in 25 cities acro...</td>\n",
       "      <td>招若干人</td>\n",
       "      <td>专业服务(咨询、人力资源、财会)</td>\n",
       "      <td>1000-5000人</td>\n",
       "      <td>- Help build up, maintain and train firm-wide ...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>4.00-5.00千/月</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>05-27发布</td>\n",
       "      <td>人工智能（语料）训练师</td>\n",
       "      <td>五险一金,定期体检,商业保险,带薪年假</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155865</th>\n",
       "      <td>西安</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>陕西汇成投资集团有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>陕西汇成集团是一家业务涉及地产、酒店、旅游、股权投资的集团企业，是在西安市西三爻改造建设发展...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>房地产,建筑/建材/工程</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>1、根据公司发展目标制定行业拓展计划，新建商务合作资源，挖掘客户真实需求并据此满足客户对交付...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>10.00-20.00千/月</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>05-10发布</td>\n",
       "      <td>人工智能 项目负责人</td>\n",
       "      <td>周末双休,带薪年假,五险一金,节日福利,包一餐</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155869</th>\n",
       "      <td>深圳</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>深圳市阿尔法智汇科技有限公司</td>\n",
       "      <td>大专</td>\n",
       "      <td>5-7年经验</td>\n",
       "      <td>深圳市阿尔法公司简介：,深圳市阿尔法智汇科技有限公司是一家以人工智能技术产品化为使命的企业，...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>计算机软件,计算机硬件</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>岗位职责：,1，原理图修改，PCB 封装制作，PCB Layout,  ,2，元器件选型，B...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>10.00-20.00千/月</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>硬件工程师</td>\n",
       "      <td>五险一金,员工旅游,专业培训,补充医疗保险,补充公积金,绩效奖金,年终奖金,出国机会</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155876</th>\n",
       "      <td>合肥</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>安徽地势坤光电科技有限公司</td>\n",
       "      <td>大专</td>\n",
       "      <td>5-7年经验</td>\n",
       "      <td>安徽地势坤光电科技有限公司(Anhui Disking Opto-Electric ...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>机械/设备/重工,电子技术/半导体/集成电路</td>\n",
       "      <td>50-150人</td>\n",
       "      <td>岗位职责：,1、 配合公司战略和业务发展需要，完善和改进项目的技术架构设计，规划未来业务平台...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>15.00-30.00千/月</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>技术总监/研发主任（机械手）</td>\n",
       "      <td>员工旅游,餐饮补贴,年终奖金,专业培训,定期体检,五险一金,弹性工作,绩效奖金,股票期权</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155878</th>\n",
       "      <td>深圳</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>武汉佰钧成技术有限责任公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>武汉佰钧成技术有限责任公司（以下简称“佰钧成”）正式成立于2006年6月，现已成为IBM、华...</td>\n",
       "      <td>招5人</td>\n",
       "      <td>计算机软件,计算机服务(系统、数据服务、维修)</td>\n",
       "      <td>10000人以上</td>\n",
       "      <td>1、负责测试工具平台开发和测试自动化工作，从整个项目角度规划自动化测试方法，...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>15.00-25.00千/月</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>自动化测试</td>\n",
       "      <td>五险一金,补充医疗保险,员工旅游,绩效奖金,年终奖金,定期体检</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       address attribute            company education experience  \\\n",
       "155859      佛山   外资（非欧美）  毕马威全球商务服务（广东）有限公司        本科       无需经验   \n",
       "155865      西安      民营公司       陕西汇成投资集团有限公司        本科     3-4年经验   \n",
       "155869      深圳      民营公司     深圳市阿尔法智汇科技有限公司        大专     5-7年经验   \n",
       "155876      合肥      民营公司      安徽地势坤光电科技有限公司        大专     5-7年经验   \n",
       "155878      深圳      民营公司      武汉佰钧成技术有限责任公司        本科     3-4年经验   \n",
       "\n",
       "                                        general_situation hiring_num  \\\n",
       "155859  Overview,KPMG China operates in 25 cities acro...       招若干人   \n",
       "155865  陕西汇成集团是一家业务涉及地产、酒店、旅游、股权投资的集团企业，是在西安市西三爻改造建设发展...        招1人   \n",
       "155869  深圳市阿尔法公司简介：,深圳市阿尔法智汇科技有限公司是一家以人工智能技术产品化为使命的企业，...        招1人   \n",
       "155876      安徽地势坤光电科技有限公司(Anhui Disking Opto-Electric ...        招1人   \n",
       "155878  武汉佰钧成技术有限责任公司（以下简称“佰钧成”）正式成立于2006年6月，现已成为IBM、华...        招5人   \n",
       "\n",
       "                       industry     members  \\\n",
       "155859         专业服务(咨询、人力资源、财会)  1000-5000人   \n",
       "155865             房地产,建筑/建材/工程    150-500人   \n",
       "155869              计算机软件,计算机硬件     50-150人   \n",
       "155876   机械/设备/重工,电子技术/半导体/集成电路     50-150人   \n",
       "155878  计算机软件,计算机服务(系统、数据服务、维修)    10000人以上   \n",
       "\n",
       "                                                 position record_date  \\\n",
       "155859  - Help build up, maintain and train firm-wide ...  2021-05-29   \n",
       "155865  1、根据公司发展目标制定行业拓展计划，新建商务合作资源，挖掘客户真实需求并据此满足客户对交付...  2021-05-29   \n",
       "155869  岗位职责：,1，原理图修改，PCB 封装制作，PCB Layout,  ,2，元器件选型，B...  2021-05-29   \n",
       "155876  岗位职责：,1、 配合公司战略和业务发展需要，完善和改进项目的技术架构设计，规划未来业务平台...  2021-05-29   \n",
       "155878          1、负责测试工具平台开发和测试自动化工作，从整个项目角度规划自动化测试方法，...  2021-05-29   \n",
       "\n",
       "                salary   tag     time           title  \\\n",
       "155859    4.00-5.00千/月  人工智能  05-27发布     人工智能（语料）训练师   \n",
       "155865  10.00-20.00千/月  人工智能  05-10发布      人工智能 项目负责人   \n",
       "155869  10.00-20.00千/月  人工智能  05-29发布           硬件工程师   \n",
       "155876  15.00-30.00千/月  人工智能  05-29发布  技术总监/研发主任（机械手）   \n",
       "155878  15.00-25.00千/月  人工智能  05-29发布           自动化测试   \n",
       "\n",
       "                                             welfare  low_salary  high_salary  \n",
       "155859                           五险一金,定期体检,商业保险,带薪年假         4.0          5.0  \n",
       "155865                       周末双休,带薪年假,五险一金,节日福利,包一餐        10.0         20.0  \n",
       "155869    五险一金,员工旅游,专业培训,补充医疗保险,补充公积金,绩效奖金,年终奖金,出国机会        10.0         20.0  \n",
       "155876  员工旅游,餐饮补贴,年终奖金,专业培训,定期体检,五险一金,弹性工作,绩效奖金,股票期权        15.0         30.0  \n",
       "155878               五险一金,补充医疗保险,员工旅游,绩效奖金,年终奖金,定期体检        15.0         25.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#筛选出人工智能岗位\n",
    "job2=job.query('tag==\"人工智能\"')\n",
    "job2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "members\n",
       "少于50人          15.694539\n",
       "5000-10000人    19.190175\n",
       "150-500人       19.207568\n",
       "10000人以上       19.640351\n",
       "50-150人        19.812098\n",
       "500-1000人      20.024986\n",
       "1000-5000人     20.108352\n",
       "Name: high_salary, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同规模公司对于人工智能岗位的最高薪资\n",
    "job2['high_salary'].groupby(job2['members']).mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱状图\n",
    "job2['high_salary'].groupby(job2['members']).mean().sort_values().plot(kind='bar')\n",
    "plt.title('不同规模公司人工智能岗位的平均最高薪资')\n",
    "plt.xlabel('公司规模')\n",
    "plt.ylabel('平均薪资')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，少于50人的公司可能由于刚刚起步，薪资相对较低，其他不论公司规模多大，薪资水平差别不大，说明薪资高低与公司规模并无直接关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同规模公司，对人工智能岗位的经验和学历要求"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "members      experience\n",
       "1000-5000人   3-4年经验        143\n",
       "             5-7年经验         94\n",
       "             2年经验           61\n",
       "             无需经验           49\n",
       "             1年经验           44\n",
       "             在校生/应届生        24\n",
       "             8-9年经验         18\n",
       "             10年以上经验        10\n",
       "10000人以上     无需经验           59\n",
       "             1年经验           35\n",
       "             3-4年经验         31\n",
       "             5-7年经验         22\n",
       "             2年经验           13\n",
       "             在校生/应届生         6\n",
       "             10年以上经验         3\n",
       "             8-9年经验          2\n",
       "150-500人     3-4年经验        228\n",
       "             5-7年经验        118\n",
       "             2年经验          102\n",
       "             1年经验           98\n",
       "             无需经验           62\n",
       "             在校生/应届生        47\n",
       "             10年以上经验        22\n",
       "             8-9年经验         18\n",
       "50-150人      3-4年经验        281\n",
       "             2年经验          154\n",
       "             5-7年经验        133\n",
       "             1年经验          115\n",
       "             无需经验           67\n",
       "             在校生/应届生        27\n",
       "             10年以上经验        24\n",
       "             8-9年经验         14\n",
       "500-1000人    3-4年经验        114\n",
       "             5-7年经验         58\n",
       "             2年经验           55\n",
       "             1年经验           49\n",
       "             无需经验           40\n",
       "             8-9年经验         14\n",
       "             10年以上经验        12\n",
       "             在校生/应届生        11\n",
       "5000-10000人  3-4年经验         16\n",
       "             2年经验           10\n",
       "             1年经验            8\n",
       "             无需经验            8\n",
       "             5-7年经验          6\n",
       "             在校生/应届生         6\n",
       "             8-9年经验          2\n",
       "             10年以上经验         1\n",
       "少于50人        3-4年经验        120\n",
       "             1年经验           93\n",
       "             5-7年经验         66\n",
       "             2年经验           54\n",
       "             无需经验           29\n",
       "             在校生/应届生        19\n",
       "             8-9年经验         15\n",
       "             10年以上经验         5\n",
       "Name: experience, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同规模公司，对人工智能岗位的经验要求\n",
    "job2['experience'].groupby(job2['members']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>experience</th>\n",
       "      <th>10年以上经验</th>\n",
       "      <th>1年经验</th>\n",
       "      <th>2年经验</th>\n",
       "      <th>3-4年经验</th>\n",
       "      <th>5-7年经验</th>\n",
       "      <th>8-9年经验</th>\n",
       "      <th>在校生/应届生</th>\n",
       "      <th>无需经验</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>members</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000-5000人</th>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "      <td>61</td>\n",
       "      <td>143</td>\n",
       "      <td>94</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000人以上</th>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150-500人</th>\n",
       "      <td>22</td>\n",
       "      <td>98</td>\n",
       "      <td>102</td>\n",
       "      <td>228</td>\n",
       "      <td>118</td>\n",
       "      <td>18</td>\n",
       "      <td>47</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-150人</th>\n",
       "      <td>24</td>\n",
       "      <td>115</td>\n",
       "      <td>154</td>\n",
       "      <td>281</td>\n",
       "      <td>133</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500-1000人</th>\n",
       "      <td>12</td>\n",
       "      <td>49</td>\n",
       "      <td>55</td>\n",
       "      <td>114</td>\n",
       "      <td>58</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000-10000人</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>少于50人</th>\n",
       "      <td>5</td>\n",
       "      <td>93</td>\n",
       "      <td>54</td>\n",
       "      <td>120</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "experience   10年以上经验  1年经验  2年经验  3-4年经验  5-7年经验  8-9年经验  在校生/应届生  无需经验\n",
       "members                                                                \n",
       "1000-5000人        10    44    61     143      94      18       24    49\n",
       "10000人以上           3    35    13      31      22       2        6    59\n",
       "150-500人          22    98   102     228     118      18       47    62\n",
       "50-150人           24   115   154     281     133      14       27    67\n",
       "500-1000人         12    49    55     114      58      14       11    40\n",
       "5000-10000人        1     8    10      16       6       2        6     8\n",
       "少于50人              5    93    54     120      66      15       19    29"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job2['experience'].groupby(job2['members']).value_counts().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制柱状图\n",
    "job3=job2['experience'].groupby(job2['members']).value_counts().unstack()\n",
    "y=job3['3-4年经验'].sort_values().values\n",
    "x=job3['3-4年经验'].sort_values().index\n",
    "plt.bar(x,y,color='blue',alpha=0.5)\n",
    "plt.title('有3-4年工作经验的岗位需求')\n",
    "plt.xlabel('公司规模')\n",
    "plt.xticks(rotation=-45)\n",
    "plt.ylabel('岗位数量')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，50-500人之间的公司对于人工智能岗位的需求量最大，这种公司往往处于发展阶段，需要大量人才。而5000人以上的这种大公司体系已经完整，需要更有技术和经验的员工，看重的往往是质量而不是数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>education</th>\n",
       "      <th>中专</th>\n",
       "      <th>中技</th>\n",
       "      <th>初中及以下</th>\n",
       "      <th>博士</th>\n",
       "      <th>大专</th>\n",
       "      <th>本科</th>\n",
       "      <th>硕士</th>\n",
       "      <th>高中</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>members</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000-5000人</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000人以上</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150-500人</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-150人</th>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500-1000人</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000-10000人</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>少于50人</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "education      中专   中技  初中及以下    博士     大专     本科    硕士    高中\n",
       "members                                                      \n",
       "1000-5000人    2.0  1.0    1.0  24.0   89.0  303.0  18.0   5.0\n",
       "10000人以上      2.0  0.0    0.0   1.0   51.0  104.0  13.0   0.0\n",
       "150-500人      5.0  3.0    0.0  10.0  186.0  451.0  39.0   1.0\n",
       "50-150人      14.0  2.0    2.0  16.0  242.0  474.0  55.0  10.0\n",
       "500-1000人     1.0  0.0    2.0   7.0   93.0  217.0  32.0   1.0\n",
       "5000-10000人   0.0  1.0    0.0   5.0   13.0   36.0   0.0   2.0\n",
       "少于50人         3.0  1.0    0.0   8.0  112.0  241.0  34.0   2.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同规模公司，对人工智能岗位的学历要求\n",
    "job2['education'].groupby(job2['members']).value_counts().unstack().fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['自动化（机器人）操作工', '快乐生活快乐工作', '金融港电销月均1.5万', '华硕普工', '普工', '机械装配工',\n",
       "       '收银员', '财税课程经理（五险一金+周末双休）', '业务员 (职位编号：010)', '咖啡师', '咖啡厅服务员',\n",
       "       '售后维修（整机）', '保安员-广州孵化器', '模具PMC生管', 'BD经理', 'BD-中山', 'BD-东莞',\n",
       "       '模具仓管/模具文员/模具仓库文员（沙井）', '生产组长', '学习规划师（松鼠Ai智适应教育）提成不封顶', '储备干部'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看高中学历的岗位\n",
    "job4=job2.query('education==\"高中\"')\n",
    "job4.title.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，对于人工智能岗位，高中学历做的往往都是一些非技术岗位。最有优势的是本科。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析热门城市的招聘岗位特征：公司分布、招聘岗位数量、公司类型、薪资范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "上海    8361\n",
       "深圳    8153\n",
       "广州    6014\n",
       "北京    3145\n",
       "杭州    2407\n",
       "武汉    2374\n",
       "成都    2271\n",
       "南京    1869\n",
       "苏州    1857\n",
       "西安    1040\n",
       "Name: address, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#各个城市的招聘岗位数量\n",
    "job5=job['address'].value_counts()\n",
    "job5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min', 'china':'https://assets.pyecharts.org/assets/maps/china'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"281c16f53f0242199b21f4ebcd908760\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
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       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
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       "                        2374\n",
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       "                    \"value\": [\n",
       "                        118.78,\n",
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       "                    \"value\": [\n",
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       "                        28.21,\n",
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       "                        811\n",
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       "                    \"value\": [\n",
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       "                    \"value\": [\n",
       "                        120.29,\n",
       "                        31.59,\n",
       "                        742\n",
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       "                    \"value\": [\n",
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       "                    \"value\": [\n",
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       "                        177\n",
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       "                    \"name\": \"\\u4e2d\\u5c71\",\n",
       "                    \"value\": [\n",
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       "                    \"name\": \"\\u5357\\u5b81\",\n",
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       "                    \"value\": [\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5f90\\u5dde\",\n",
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       "                {\n",
       "                    \"name\": \"\\u6e56\\u5dde\",\n",
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       "                        120.1,\n",
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       "                {\n",
       "                    \"name\": \"\\u7ecd\\u5174\",\n",
       "                    \"value\": [\n",
       "                        120.58,\n",
       "                        30.01,\n",
       "                        65\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u957f\\u6625\",\n",
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       "                {\n",
       "                    \"name\": \"\\u829c\\u6e56\",\n",
       "                    \"value\": [\n",
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       "                        31.33,\n",
       "                        60\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e38\\u719f\",\n",
       "                    \"value\": [\n",
       "                        120.74,\n",
       "                        31.64,\n",
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       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u70df\\u53f0\",\n",
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       "                        52\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u95e8\",\n",
       "                    \"value\": [\n",
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       "                        49\n",
       "                    ]\n",
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       "                        45\n",
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       "                    \"value\": [\n",
       "                        121.420757,\n",
       "                        28.656386,\n",
       "                        44\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u91d1\\u534e\",\n",
       "                    \"value\": [\n",
       "                        119.64,\n",
       "                        29.12,\n",
       "                        44\n",
       "                    ]\n",
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       "                {\n",
       "                    \"name\": \"\\u76d0\\u57ce\",\n",
       "                    \"value\": [\n",
       "                        120.13,\n",
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       "                        42\n",
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       "                {\n",
       "                    \"name\": \"\\u7ef5\\u9633\",\n",
       "                    \"value\": [\n",
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       "                        40\n",
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       "                {\n",
       "                    \"name\": \"\\u6842\\u6797\",\n",
       "                    \"value\": [\n",
       "                        110.28,\n",
       "                        25.29,\n",
       "                        39\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9547\\u6c5f\",\n",
       "                    \"value\": [\n",
       "                        119.44,\n",
       "                        32.2,\n",
       "                        36\n",
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       "                    \"value\": [\n",
       "                        103.73,\n",
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       "                        34\n",
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       "                    \"name\": \"\\u6e05\\u8fdc\",\n",
       "                    \"value\": [\n",
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       "                        34\n",
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       "                {\n",
       "                    \"name\": \"\\u8087\\u5e86\",\n",
       "                    \"value\": [\n",
       "                        112.44,\n",
       "                        23.05,\n",
       "                        33\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d1b\\u9633\",\n",
       "                    \"value\": [\n",
       "                        112.44,\n",
       "                        34.7,\n",
       "                        33\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u592a\\u539f\",\n",
       "                    \"value\": [\n",
       "                        112.53,\n",
       "                        37.87,\n",
       "                        31\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c55\\u5934\",\n",
       "                    \"value\": [\n",
       "                        116.69,\n",
       "                        23.39,\n",
       "                        30\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u626c\\u5dde\",\n",
       "                    \"value\": [\n",
       "                        119.42,\n",
       "                        32.39,\n",
       "                        29\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u4e1c\\u7701\",\n",
       "                    \"value\": [\n",
       "                        113.26653,\n",
       "                        23.132191,\n",
       "                        28\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u592a\\u4ed3\",\n",
       "                    \"value\": [\n",
       "                        121.1,\n",
       "                        31.45,\n",
       "                        27\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8944\\u9633\",\n",
       "                    \"value\": [\n",
       "                        112.2,\n",
       "                        32.08,\n",
       "                        26\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u547c\\u548c\\u6d69\\u7279\",\n",
       "                    \"value\": [\n",
       "                        111.65,\n",
       "                        40.82,\n",
       "                        26\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b81\\u5fb7\",\n",
       "                    \"value\": [\n",
       "                        119.31,\n",
       "                        26.39,\n",
       "                        25\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d77\\u5b81\",\n",
       "                    \"value\": [\n",
       "                        120.42,\n",
       "                        30.32,\n",
       "                        24\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cf0\\u5dde\",\n",
       "                    \"value\": [\n",
       "                        119.9,\n",
       "                        32.49,\n",
       "                        24\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u67f3\\u5dde\",\n",
       "                    \"value\": [\n",
       "                        109.4,\n",
       "                        24.33,\n",
       "                        24\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e4c\\u9c81\\u6728\\u9f50\",\n",
       "                    \"value\": [\n",
       "                        87.68,\n",
       "                        43.77,\n",
       "                        23\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8fde\\u4e91\\u6e2f\",\n",
       "                    \"value\": [\n",
       "                        119.16,\n",
       "                        34.59,\n",
       "                        22\n",
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       "                {\n",
       "                    \"name\": \"\\u868c\\u57e0\",\n",
       "                    \"value\": [\n",
       "                        117.21,\n",
       "                        32.56,\n",
       "                        22\n",
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       "                {\n",
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       "                    \"value\": [\n",
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       "                        22\n",
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       "                {\n",
       "                    \"name\": \"\\u9a6c\\u978d\\u5c71\",\n",
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       "                        22\n",
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       "                        21\n",
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       "                    \"value\": [\n",
       "                        114.58,\n",
       "                        27.07,\n",
       "                        21\n",
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       "                {\n",
       "                    \"name\": \"\\u6c5f\\u82cf\\u7701\",\n",
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       "                    \"value\": [\n",
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       "                        20\n",
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       "                    \"name\": \"\\u8386\\u7530\",\n",
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       "                        24.26,\n",
       "                        20\n",
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       "                        120.152791,\n",
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       "                        18\n",
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       "                    \"value\": [\n",
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       "                {\n",
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       "                        27.83,\n",
       "                        15\n",
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       "                {\n",
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       "                {\n",
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       "                        28.112444,\n",
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       "                        30.7,\n",
       "                        14\n",
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       "                {\n",
       "                    \"name\": \"\\u94f6\\u5ddd\",\n",
       "                    \"value\": [\n",
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       "                        38.47,\n",
       "                        14\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        32.4,\n",
       "                        14\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c5f\\u897f\\u7701\",\n",
       "                    \"value\": [\n",
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       "                        28.675696,\n",
       "                        14\n",
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       "                {\n",
       "                    \"name\": \"\\u56db\\u5ddd\\u7701\",\n",
       "                    \"value\": [\n",
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       "                        30.651651,\n",
       "                        14\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b9c\\u5bbe\",\n",
       "                    \"value\": [\n",
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       "                        29.77,\n",
       "                        13\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8bb8\\u660c\",\n",
       "                    \"value\": [\n",
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       "                        34.01,\n",
       "                        13\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        30.12,\n",
       "                        13\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b5d\\u611f\",\n",
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       "                        30.56,\n",
       "                        13\n",
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       "                {\n",
       "                    \"name\": \"\\u5e7f\\u897f\",\n",
       "                    \"value\": [\n",
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       "                        22.815478,\n",
       "                        12\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6ec1\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        32.18,\n",
       "                        12\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8346\\u5dde\",\n",
       "                    \"value\": [\n",
       "                        112.239741,\n",
       "                        30.335165,\n",
       "                        12\n",
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       "                        11\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5357\\u7701\",\n",
       "                    \"value\": [\n",
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       "                        11\n",
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       "                        34.265472,\n",
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       "                        11\n",
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       "                        11\n",
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       "                        26.100779,\n",
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       "                        11\n",
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       "                {\n",
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       "                        11\n",
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       "                {\n",
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       "                        11\n",
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       "                        10\n",
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       "                        10\n",
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       "                    \"name\": \"\\u8346\\u95e8\",\n",
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       "                        9\n",
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       "                {\n",
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       "                        9\n",
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       "                {\n",
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       "                        9\n",
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       "                {\n",
       "                    \"name\": \"\\u6cb3\\u5317\\u7701\",\n",
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       "                        9\n",
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       "                        9\n",
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       "                        9\n",
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       "                        9\n",
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       "                {\n",
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       "                        8\n",
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       "                {\n",
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       "                        8\n",
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       "                {\n",
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       "                        8\n",
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       "                {\n",
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       "                        35.18,\n",
       "                        8\n",
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       "                {\n",
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       "                        8\n",
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       "                        8\n",
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       "                        33.96,\n",
       "                        8\n",
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       "                {\n",
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       "                        8\n",
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       "                {\n",
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       "                        7\n",
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       "                        7\n",
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       "                        7\n",
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       "                        7\n",
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       "                        7\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        6\n",
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       "                        5\n",
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       "                {\n",
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       "                        5\n",
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       "                        5\n",
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       "                        5\n",
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       "                {\n",
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       "                {\n",
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       "                        34.76,\n",
       "                        5\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b89\\u9633\",\n",
       "                    \"value\": [\n",
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       "                        36.1,\n",
       "                        5\n",
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       "                {\n",
       "                    \"name\": \"\\u9042\\u5b81\",\n",
       "                    \"value\": [\n",
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       "                        30.31,\n",
       "                        4\n",
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       "                {\n",
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       "                        32.37,\n",
       "                        4\n",
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       "                {\n",
       "                    \"name\": \"\\u81ea\\u8d21\",\n",
       "                    \"value\": [\n",
       "                        104.778442,\n",
       "                        29.33903,\n",
       "                        4\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        25.27,\n",
       "                        4\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c55\\u5c3e\",\n",
       "                    \"value\": [\n",
       "                        115.375279,\n",
       "                        22.786211,\n",
       "                        4\n",
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       "                {\n",
       "                    \"name\": \"\\u83cf\\u6cfd\",\n",
       "                    \"value\": [\n",
       "                        115.480656,\n",
       "                        35.23375,\n",
       "                        4\n",
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       "                {\n",
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       "                    \"value\": [\n",
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       "                        34.26,\n",
       "                        4\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7518\\u8083\\u7701\",\n",
       "                    \"value\": [\n",
       "                        103.826308,\n",
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       "                        4\n",
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       "                {\n",
       "                    \"name\": \"\\u968f\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        31.42,\n",
       "                        4\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6ee8\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        37.36,\n",
       "                        4\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        25.51,\n",
       "                        4\n",
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       "                {\n",
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       "                    \"value\": [\n",
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       "                        27.44,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        36.18,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u897f\\u85cf\",\n",
       "                    \"value\": [\n",
       "                        91.117212,\n",
       "                        29.646922,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cb3\\u6c60\",\n",
       "                    \"value\": [\n",
       "                        108.03,\n",
       "                        24.42,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d4e\\u6e90\",\n",
       "                    \"value\": [\n",
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       "                        35.04,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7126\\u4f5c\",\n",
       "                    \"value\": [\n",
       "                        113.21,\n",
       "                        35.24,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u94dc\\u4ec1\",\n",
       "                    \"value\": [\n",
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       "                        27.43,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6000\\u5316\",\n",
       "                    \"value\": [\n",
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       "                        27.33,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8861\\u6c34\",\n",
       "                    \"value\": [\n",
       "                        115.72,\n",
       "                        37.72,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9075\\u4e49\",\n",
       "                    \"value\": [\n",
       "                        106.9,\n",
       "                        27.7,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5409\\u6797\\u7701\",\n",
       "                    \"value\": [\n",
       "                        125.32599,\n",
       "                        43.896536,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9526\\u5dde\",\n",
       "                    \"value\": [\n",
       "                        121.15,\n",
       "                        41.13,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6cf0\\u5174\",\n",
       "                    \"value\": [\n",
       "                        120.01,\n",
       "                        32.1,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5468\\u53e3\",\n",
       "                    \"value\": [\n",
       "                        114.38,\n",
       "                        33.37,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5185\\u8499\\u53e4\",\n",
       "                    \"value\": [\n",
       "                        111.765617,\n",
       "                        40.817498,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u65b0\\u7586\",\n",
       "                    \"value\": [\n",
       "                        87.627704,\n",
       "                        43.793026,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8fbd\\u5b81\\u7701\",\n",
       "                    \"value\": [\n",
       "                        123.42944,\n",
       "                        41.835441,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b81\\u590f\",\n",
       "                    \"value\": [\n",
       "                        106.258754,\n",
       "                        38.471317,\n",
       "                        3\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9a7b\\u9a6c\\u5e97\",\n",
       "                    \"value\": [\n",
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       "                        32.58,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u79e6\\u7687\\u5c9b\",\n",
       "                    \"value\": [\n",
       "                        119.57,\n",
       "                        39.95,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5929\\u6c34\",\n",
       "                    \"value\": [\n",
       "                        105.42,\n",
       "                        34.37,\n",
       "                        3\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9102\\u5c14\\u591a\\u65af\",\n",
       "                    \"value\": [\n",
       "                        109.781327,\n",
       "                        39.608266,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u897f\\u660c\",\n",
       "                    \"value\": [\n",
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       "                        27.54,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d77\\u5357\\u7701\",\n",
       "                    \"value\": [\n",
       "                        110.349228,\n",
       "                        20.017377,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u68a7\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        23.29,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u90f4\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        25.46,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u7406\",\n",
       "                    \"value\": [\n",
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       "                        25.34,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c38\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        26.13,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9756\\u6c5f\",\n",
       "                    \"value\": [\n",
       "                        120.17,\n",
       "                        32.02,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u666f\\u5fb7\\u9547\",\n",
       "                    \"value\": [\n",
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       "                        29.17,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5e7f\\u5143\",\n",
       "                    \"value\": [\n",
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       "                        32.28,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4ed9\\u6843\",\n",
       "                    \"value\": [\n",
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       "                        30.22,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9752\\u6d77\\u7701\",\n",
       "                    \"value\": [\n",
       "                        101.780199,\n",
       "                        36.620901,\n",
       "                        2\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6587\\u5c71\",\n",
       "                    \"value\": [\n",
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       "                        23.37,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d44\\u9633\",\n",
       "                    \"value\": [\n",
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       "                        30.09,\n",
       "                        2\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5f00\\u5c01\",\n",
       "                    \"value\": [\n",
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       "                        34.79,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6f5c\\u6c5f\",\n",
       "                    \"value\": [\n",
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       "                        30.26,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u897f\\u53cc\\u7248\\u7eb3\",\n",
       "                    \"value\": [\n",
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       "                        22.02,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6f6e\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        23.68,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5fb7\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        37.45,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6069\\u65bd\",\n",
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       "                        30.16,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u90b5\\u9633\",\n",
       "                    \"value\": [\n",
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       "                        27.14,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8fbe\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        31.22,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u664b\\u57ce\",\n",
       "                    \"value\": [\n",
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       "                        35.3,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u65b0\\u4f59\",\n",
       "                    \"value\": [\n",
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       "                        27.48,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6500\\u679d\\u82b1\",\n",
       "                    \"value\": [\n",
       "                        101.718637,\n",
       "                        26.582347,\n",
       "                        2\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6fee\\u9633\",\n",
       "                    \"value\": [\n",
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       "                        35.44,\n",
       "                        2\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6c49\\u4e2d\",\n",
       "                    \"value\": [\n",
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       "                        33.04,\n",
       "                        2\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u63ed\\u9633\",\n",
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       "                        23.55,\n",
       "                        2\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u662d\\u901a\",\n",
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       "                        27.2,\n",
       "                        2\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6f84\\u8fc8\",\n",
       "                    \"value\": [\n",
       "                        110,\n",
       "                        19.73,\n",
       "                        2\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u76d8\\u9526\",\n",
       "                    \"value\": [\n",
       "                        122.070714,\n",
       "                        41.119997,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5b9d\\u9e21\",\n",
       "                    \"value\": [\n",
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       "                        34.38,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e3d\\u6c5f\",\n",
       "                    \"value\": [\n",
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       "                        26.88,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
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       "                        1\n",
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       "                },\n",
       "                {\n",
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       "                        28.14,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u94c1\\u5cad\",\n",
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       "                        42.18,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5415\\u6881\",\n",
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       "                        37.52,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u664b\\u4e2d\",\n",
       "                    \"value\": [\n",
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       "                        37.68,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        36.6,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5305\\u5934\",\n",
       "                    \"value\": [\n",
       "                        110,\n",
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       "                        1\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        34.52,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        37.05,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9e64\\u58c1\",\n",
       "                    \"value\": [\n",
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       "                        35.54,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9633\\u6cc9\",\n",
       "                    \"value\": [\n",
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       "                        37.85,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e4c\\u6d77\",\n",
       "                    \"value\": [\n",
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       "                        39.4,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
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       "                        36.45,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u7ea2\\u6cb3\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        23.37,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6bd5\\u8282\",\n",
       "                    \"value\": [\n",
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       "                        27.18,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u547c\\u4f26\\u8d1d\\u5c14\",\n",
       "                    \"value\": [\n",
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       "                        49.22,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5929\\u95e8\",\n",
       "                    \"value\": [\n",
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       "                        30.39,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6f2f\\u6cb3\",\n",
       "                    \"value\": [\n",
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       "                        33.33,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9f50\\u9f50\\u54c8\\u5c14\",\n",
       "                    \"value\": [\n",
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       "                        47.33,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u540c\",\n",
       "                    \"value\": [\n",
       "                        113.3,\n",
       "                        40.12,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e34\\u6c7e\",\n",
       "                    \"value\": [\n",
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       "                        36.08,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u767d\\u57ce\",\n",
       "                    \"value\": [\n",
       "                        122.5,\n",
       "                        45.38,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6765\\u5bbe\",\n",
       "                    \"value\": [\n",
       "                        109.23,\n",
       "                        23.73,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4eb3\\u5dde\",\n",
       "                    \"value\": [\n",
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       "                        33.52,\n",
       "                        1\n",
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       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u96c5\\u5b89\",\n",
       "                    \"value\": [\n",
       "                        102.59,\n",
       "                        29.59,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4fe1\\u9633\",\n",
       "                    \"value\": [\n",
       "                        114.04,\n",
       "                        32.07,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e39\\u9633\",\n",
       "                    \"value\": [\n",
       "                        119.32,\n",
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       "                        1\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
       "                        112.05,\n",
       "                        32.42,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u963f\\u514b\\u82cf\",\n",
       "                    \"value\": [\n",
       "                        80.19,\n",
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       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u8d64\\u5cf0\",\n",
       "                    \"value\": [\n",
       "                        118.87,\n",
       "                        42.28,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
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       "                    \"value\": [\n",
       "                        113.5,\n",
       "                        27.37,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
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       "                        131.11,\n",
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       "                        1\n",
       "                    ]\n",
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       "                    \"value\": [\n",
       "                        109.47,\n",
       "                        36.6,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e07\\u5b81\",\n",
       "                    \"value\": [\n",
       "                        110.4,\n",
       "                        18.8,\n",
       "                        1\n",
       "                    ]\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e1c\\u65b9\",\n",
       "                    \"value\": [\n",
       "                        108.63,\n",
       "                        19.1,\n",
       "                        1\n",
       "                    ]\n",
       "                }\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"geo\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"geo\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"formatter\": function (params) {        return params.name + ' : ' + params.value[2];    },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u5c97\\u4f4d\\u6570\\u91cf\\u5168\\u56fd\\u5206\\u5e03\\u56fe\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"continuous\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 3000,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 140,\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"geo\": {\n",
       "        \"map\": \"china\",\n",
       "        \"roam\": true,\n",
       "        \"aspectScale\": 0.75,\n",
       "        \"nameProperty\": \"name\",\n",
       "        \"selectedMode\": false,\n",
       "        \"emphasis\": {}\n",
       "    }\n",
       "};\n",
       "                chart_281c16f53f0242199b21f4ebcd908760.setOption(option_281c16f53f0242199b21f4ebcd908760);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x257ebabc730>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#不同岗位数量在全国范围内的分布\n",
    "count=job['address'].value_counts()\n",
    "\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Geo\n",
    "#from pyecharts.faker import Faker\n",
    "\n",
    "c = (\n",
    "    Geo(\n",
    "        is_ignore_nonexistent_coord= True,#忽略不存在的地点\n",
    "    )\n",
    "    .add_schema(maptype=\"china\",     # 地图类型\n",
    "#                 aspect_scale= 0.75,  #地图的长宽比。\n",
    "               )\n",
    "    \n",
    "    .add(\"geo\", \n",
    "         [list(count) for count in zip(count.index.tolist(), count.tolist())],#传入的值\n",
    "         \n",
    "         point_size=1000,\n",
    "         symbol_size=8,  #标记点的大小\n",
    "         color='red',   #标记点颜色\n",
    "        )\n",
    "    \n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    \n",
    "    .set_global_opts(\n",
    "        visualmap_opts=opts.VisualMapOpts(min_=0,max_=3000),#设置滑动标签的大小值\n",
    "        title_opts=opts.TitleOpts(title=\"岗位数量全国分布图\")\n",
    "    )\n",
    ")\n",
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，招聘岗位比较密集的区域是东南沿海等发达地区，而东北和西北地区的岗位数量较少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>attribute</th>\n",
       "      <th>company</th>\n",
       "      <th>education</th>\n",
       "      <th>experience</th>\n",
       "      <th>general_situation</th>\n",
       "      <th>hiring_num</th>\n",
       "      <th>industry</th>\n",
       "      <th>members</th>\n",
       "      <th>position</th>\n",
       "      <th>record_date</th>\n",
       "      <th>salary</th>\n",
       "      <th>tag</th>\n",
       "      <th>time</th>\n",
       "      <th>title</th>\n",
       "      <th>welfare</th>\n",
       "      <th>low_salary</th>\n",
       "      <th>high_salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>广州</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>佰聆数据股份有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>2年经验</td>\n",
       "      <td>佰聆数据股份有限公司（证券简称：佰聆数据，股票代码：833619）成立于2008年，早在业界...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>互联网/电子商务,计算机软件</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>岗位职责：,1、负责需求分析、数据调研以及数据统计分析等工作；,2、负责日常与客户的沟通交流...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>10.00-15.00千/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>五险一金,餐饮补贴,通讯补贴,弹性工作,定期体检,年终奖金</td>\n",
       "      <td>10.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>北京</td>\n",
       "      <td>国企</td>\n",
       "      <td>国家电投集团数字科技有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>8-9年经验</td>\n",
       "      <td>信息公司组建于2013年8月，是国家电力投资集团有限公司的控股子公司，是集团...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>计算机服务(系统、数据服务、维修),计算机硬件</td>\n",
       "      <td>1000-5000人</td>\n",
       "      <td>岗位职责： ,    1、面向能源领域企业，提供数据预处理、加工、分析解决方案，负责进行解决...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>20.00-25.00千/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>领军人才—数据分析师</td>\n",
       "      <td>五险一金,补充医疗保险,交通补贴,绩效奖金,年终奖金,定期体检,专业培训,通讯补贴,餐饮补贴</td>\n",
       "      <td>20.00</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>北京</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>北京万华恒信信息技术有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>3-4年经验</td>\n",
       "      <td>北京万华恒信信息技术有限公司成立于2006年，主营开发、生产计算机软件；自产产品、通讯、电子...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>计算机软件,互联网/电子商务</td>\n",
       "      <td>150-500人</td>\n",
       "      <td>1、负责业务日常数据处理；,2、对业务数据进行分析、挖掘、清洗, 满足研发和运营等部门的业务...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>12.00-15.00千/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>五险一金,补充医疗保险,员工旅游,交通补贴,绩效奖金,年终奖金,定期体检</td>\n",
       "      <td>12.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>北京</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>氢源嘉创（杭州）新能源科技有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>5-7年经验</td>\n",
       "      <td>锦嘉（杭州）新能源科技有限公司成立于2019年，是一家专业从事加氢站投资与建设的...</td>\n",
       "      <td>招1人</td>\n",
       "      <td>新能源,石油/化工/矿产/地质</td>\n",
       "      <td>少于50人</td>\n",
       "      <td>1、深入理解和分析数据，对数据进行可视化处理；,任职要求：,1、本科及以上学历、数学、统计类...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>10.00-15.00千/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-29发布</td>\n",
       "      <td>数据分析工程师</td>\n",
       "      <td>五险一金</td>\n",
       "      <td>10.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>北京</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>北京天智鲲鹏技术有限公司</td>\n",
       "      <td>本科</td>\n",
       "      <td>无需经验</td>\n",
       "      <td>北京天智数据总部位于中国首都北京，是***高新技术企业、中关村高新技术企业及新产品新技术认定...</td>\n",
       "      <td>招5人</td>\n",
       "      <td>计算机软件</td>\n",
       "      <td>少于50人</td>\n",
       "      <td>1、有较强的SQL能力，需熟练掌握公司产品；,2、能撰写公司产品方案相关的PPT；,3、良好...</td>\n",
       "      <td>2021-05-29</td>\n",
       "      <td>6.67-8.33千/月</td>\n",
       "      <td>数据分析</td>\n",
       "      <td>05-28发布</td>\n",
       "      <td>数据分析师</td>\n",
       "      <td>五险一金,年终奖金,绩效奖金,周末双休</td>\n",
       "      <td>6.67</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   address attribute            company education experience  \\\n",
       "5       广州      民营公司         佰聆数据股份有限公司        本科       2年经验   \n",
       "6       北京        国企     国家电投集团数字科技有限公司        本科     8-9年经验   \n",
       "7       北京      民营公司     北京万华恒信信息技术有限公司        本科     3-4年经验   \n",
       "11      北京      民营公司  氢源嘉创（杭州）新能源科技有限公司        本科     5-7年经验   \n",
       "18      北京      民营公司       北京天智鲲鹏技术有限公司        本科       无需经验   \n",
       "\n",
       "                                    general_situation hiring_num  \\\n",
       "5   佰聆数据股份有限公司（证券简称：佰聆数据，股票代码：833619）成立于2008年，早在业界...        招1人   \n",
       "6           信息公司组建于2013年8月，是国家电力投资集团有限公司的控股子公司，是集团...        招1人   \n",
       "7   北京万华恒信信息技术有限公司成立于2006年，主营开发、生产计算机软件；自产产品、通讯、电子...        招1人   \n",
       "11        锦嘉（杭州）新能源科技有限公司成立于2019年，是一家专业从事加氢站投资与建设的...        招1人   \n",
       "18  北京天智数据总部位于中国首都北京，是***高新技术企业、中关村高新技术企业及新产品新技术认定...        招5人   \n",
       "\n",
       "                   industry     members  \\\n",
       "5            互联网/电子商务,计算机软件    150-500人   \n",
       "6   计算机服务(系统、数据服务、维修),计算机硬件  1000-5000人   \n",
       "7            计算机软件,互联网/电子商务    150-500人   \n",
       "11          新能源,石油/化工/矿产/地质       少于50人   \n",
       "18                    计算机软件       少于50人   \n",
       "\n",
       "                                             position record_date  \\\n",
       "5   岗位职责：,1、负责需求分析、数据调研以及数据统计分析等工作；,2、负责日常与客户的沟通交流...  2021-05-29   \n",
       "6   岗位职责： ,    1、面向能源领域企业，提供数据预处理、加工、分析解决方案，负责进行解决...  2021-05-29   \n",
       "7   1、负责业务日常数据处理；,2、对业务数据进行分析、挖掘、清洗, 满足研发和运营等部门的业务...  2021-05-29   \n",
       "11  1、深入理解和分析数据，对数据进行可视化处理；,任职要求：,1、本科及以上学历、数学、统计类...  2021-05-29   \n",
       "18  1、有较强的SQL能力，需熟练掌握公司产品；,2、能撰写公司产品方案相关的PPT；,3、良好...  2021-05-29   \n",
       "\n",
       "            salary   tag     time       title  \\\n",
       "5   10.00-15.00千/月  数据分析  05-29发布       数据分析师   \n",
       "6   20.00-25.00千/月  数据分析  05-29发布  领军人才—数据分析师   \n",
       "7   12.00-15.00千/月  数据分析  05-29发布       数据分析师   \n",
       "11  10.00-15.00千/月  数据分析  05-29发布     数据分析工程师   \n",
       "18    6.67-8.33千/月  数据分析  05-28发布       数据分析师   \n",
       "\n",
       "                                           welfare  low_salary  high_salary  \n",
       "5                    五险一金,餐饮补贴,通讯补贴,弹性工作,定期体检,年终奖金       10.00        15.00  \n",
       "6   五险一金,补充医疗保险,交通补贴,绩效奖金,年终奖金,定期体检,专业培训,通讯补贴,餐饮补贴       20.00        25.00  \n",
       "7             五险一金,补充医疗保险,员工旅游,交通补贴,绩效奖金,年终奖金,定期体检       12.00        15.00  \n",
       "11                                            五险一金       10.00        15.00  \n",
       "18                             五险一金,年终奖金,绩效奖金,周末双休        6.67         8.33  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#筛选4大热门城市的岗位信息\n",
    "job6=job.query('address==\"上海\" or address==\"深圳\" or address==\"广州\" or address==\"北京\"')\n",
    "job6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "数据分析师                                                  383\n",
       "算法工程师                                                  346\n",
       "Java开发工程师                                              327\n",
       "嵌入式软件工程师                                               297\n",
       "软件工程师                                                  240\n",
       "                                                      ... \n",
       "京东自营店店长                                                  1\n",
       "功能安全APP开发工程师 (车载网联方向)(SH-EC) (职位编号：RYXQ20201230356)      1\n",
       "弱电工程师（大疆天空之城）                                            1\n",
       "资深软件研发工程师(J10670)                                        1\n",
       "品质主管（包住，有餐补，广州上班）                                        1\n",
       "Name: title, Length: 15219, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#热门城市的招聘岗位及数量\n",
    "job6['title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "民营公司       17366\n",
       "上市公司        2377\n",
       "合资          1724\n",
       "国企          1370\n",
       "外资（非欧美）     1263\n",
       "外资（欧美）       850\n",
       "创业公司         364\n",
       "事业单位         287\n",
       "非营利组织         67\n",
       "外企代表处          5\n",
       "Name: attribute, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#热门城市的公司类型\n",
    "job6['attribute'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制饼状图\n",
    "plt.pie(job6['attribute'].value_counts(),autopct='%.2f%%',labels=job6['attribute'].value_counts().index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，热门城市招聘的公司大都是民营公司。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.00-15.00千/月    2842\n",
       "15.00-20.00千/月    2039\n",
       "15.00-25.00千/月    1380\n",
       "10.00-20.00千/月    1150\n",
       "8.00-10.00千/月     1123\n",
       "                  ... \n",
       "5.00-8.10千/月         1\n",
       "23.33-25.00千/月       1\n",
       "28.00-32.00千/月       1\n",
       "19.17-28.33千/月       1\n",
       "22.00-34.00千/月       1\n",
       "Name: salary, Length: 717, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#热门城市公司的薪资范围\n",
    "job6['salary'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结：\n",
    "##### 1.北上广深等经济发达的地区对互联网行业的需求量大，岗位以数据分析和算法为主，企业类型以民营企业为主，主要是本科最多，经验越丰富，薪资水平越高。\n",
    "##### 2.对于人工智能岗位，企业规模为50-500人的需求量大，学历要求以本科为主，有3-4年工作经验更有优势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
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    "left": "10px",
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